US20160245279A1 - Real time machine learning based predictive and preventive maintenance of vacuum pump - Google Patents
Real time machine learning based predictive and preventive maintenance of vacuum pump Download PDFInfo
- Publication number
- US20160245279A1 US20160245279A1 US14/628,322 US201514628322A US2016245279A1 US 20160245279 A1 US20160245279 A1 US 20160245279A1 US 201514628322 A US201514628322 A US 201514628322A US 2016245279 A1 US2016245279 A1 US 2016245279A1
- Authority
- US
- United States
- Prior art keywords
- sensor data
- data
- machine learning
- blower
- vacuum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B51/00—Testing machines, pumps, or pumping installations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/025—Details with respect to the testing of engines or engine parts
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/26—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
Definitions
- the present invention generally relates to Internet of Things (IoT), and more particularly relates to an IoT-based system for predictive and preventive maintenance of machines that uses a blower and a pump, through machine learning and physics based modeling of physical parameters like vibration, sound, temperature monitored by machine wearable and other related sensors.
- IoT Internet of Things
- IoT Internet of Things
- M2M Machine to Machine
- an entity becomes a “thing” of an M2M IoT especially, when the entity is attached with one or more sensors capable of capturing one or more types of data pertaining to: segregation of the data (if applicable); selective communication of each segregation of data to one or more fellow “things”; reception of one or more control commands (or instructions) from one or more fellow “things” wherein, the control commands are based on the data received by the one or more fellow “things”; and execution of the commands resulting in manipulation or “management” of an operation of the corresponding entity. Therefore, in an IoT-enabled system, the “things” basically manage themselves without any human intervention, thus drastically improving the efficiency thereof.
- EP Patent No. 1836576 B1 discusses a diagnostic method of failure protection of vacuum pumps. Based on comparison of the currently evaluated diagnostics analysis results and an initial data, maintenance engineers would decide the replacement of the considered vacuum pump, according to the evaluated pump performance indicators. However, in this prior art invention, there is no mention of machine learning or use of machine wearable sensors. Also, the remedial decisions are left to the maintenance engineers.
- US Patent application 20120209569 A1 discusses a method for predicting a failure in rotation of a rotor of a vacuum pump.
- the prior art invention fails to disclose machine learning capabilities and also is dependent on an observation time prediction window. Further, the prior art fails to disclose machine wearable sensors and Internet of things.
- U.S. Pat. No. 7,882,394 B2 discusses fault diagnostics through a data collection module.
- the prior art discloses a system for condition monitoring and fault diagnosis that includes: a data collection function that acquires time histories of selected variables for one or more of components; a pre-processing function that calculates specified characteristics of time histories; an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the components, and a reasoning function for determining the condition of the components from one or more hypotheses.
- the prior art invention fails to suggest the concept of IoT. Further, the prior art invention does not mention machine learning for effective predictive or preventive maintenance of vacuum pumps or similar devices.
- a method of machine learning architecture includes a step of: receiving data from machine wearable sensors placed on a motor (henceforth motor sensor data) and a blower (henceforth a blower sensor data) over a communications network.
- the machine wearable sensors can be selected from a group consisting of vibration sensors, temperature sensors, magnetic field sensors, gyroscope and its combinations thereof.
- the sensor type can be single silicon or MEMS (Micro-electromechanical systems) type.
- the motor or blower sensor data is classified into one of a vacuum state sensor data and vacuum break state (where rotor is switched off and the revolution of the rotor is gradually damping in vacuum medium) sensor data, wherein the vacuum state sensor data is further analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range.
- the vacuum break state sensor data is then classified into clean filter category and clogged filter category and an alarm is raised if the real time data of sensors belonging to clogged filter category is detected.
- Vacuum state data is further classified based on a multi-class learning model which classifies a pump running with machine oil into clean, old, leaked or overfilled classes. If the sensor data suggest neither, it is classified under uncategorized bearing issues.
- the blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect deficient oil level and deficient oil structure.
- An IoT based machine learning architecture includes: a vacuum pump associated with a blower and a motor coupled with one or more machine wearable sensors; a communications network; and a mobile application associated with a mobile device.
- the mobile application is communicatively coupled to one or more machine wearable sensors, over the communications network.
- the mobile application can be replaced by a PC based communication such as a PC based app, as well.
- the machine learning architecture receives sensor data from the blower and the motor and classifies the motor sensor data into vacuum state sensor data and break state sensor data. Also, the machine learning architecture analyzes the vacuum state sensor data to detect an operating vacuum level and an alarm is_raised when the vacuum state sensor data exceeds a pre-defined safety range.
- the machine learning architecture classifies vacuum break data into clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected, and the machine learning architecture further analyzes the blower sensor data in association with the motor sensor data through a machine learning algorithm in order to detect at least one of a deficient oil level and a deficient oil structure.
- the present invention relates to an Internet of Things (IoT) based system for overseeing process control and predictive maintenance of a machine or a network of machines by employing machine wearable sensors.
- the IoT based system includes a plurality of machine-wearable sensors, secured to the exterior of the machine. These sensors can be any combination of Temperature sensors, Vibration sensors, Magnetometer, Gyroscope. Each sensor is capable of transmitting captured data wirelessly over a communications network.
- the IoT based system further includes a sensor network for receiving and transmitting the captured data over a communications network.
- the system also includes: a machine learning algorithm engine capable of receiving data from the sensor network and processing the received data to recognize one of a pattern and a deviation to issue an alarm and appropriate control commands pertaining to the machine.
- the system further includes one or more control modules disposed in operative communication with the control panel of the machine, wherein the control module is capable of receiving the control commands over a communications network and executing the control commands.
- FIG. 1 is a diagrammatic representation of a machine learning architecture, according to one or more embodiments.
- FIG. 2 is a diagrammatic representation of a data processing system capable of processing a set of instructions to perform any one or more of the methodologies herein, according to one embodiment.
- FIG. 3 is a process flow diagram detailing the operations of a method of a machine learning architecture, according to one or more embodiments.
- FIG. 4 is an exemplary representation of data on a mobile application associated with the machine learning architecture, according to one or more embodiments.
- FIG. 5 is an exemplary representation of a mobile status dashboard for a dryer.
- the mobile status dashboard displays a status of a process anytime, anywhere through a connection with an internet.
- FIG. 6 is an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device generated automatically by a Dryer, according to one or more embodiments.
- FIG. 7 is an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device generated automatically by a Vacuum pump, according to one or more embodiments.
- FIG. 8 is a representation of a mobile application tracking abusive operations for preventive maintenance so that pumps may last longer from real time vibration data, according to one embodiment.
- FIG. 9 is a representation of a real time status of a vacuum pump as seen on a mobile application, according to one embodiments.
- FIG. 10 is a representation of real time trend of drying-indicating clean dryer against dryers with clogged filter, according to one embodiment.
- FIG. 11 is a representation of normal oil levels against low oil levels, according to one embodiment.
- FIG. 12 is a clustering diagram showing pump temperature and vibration indicating different possible cluster of operation based on oil levels, according to one embodiment.
- FIG. 13 is a representation of normalized vibration against time, according to one embodiment.
- FIG. 14A through FIG. 14C illustrates graphical representations of vibration fault detection using transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components, respectively of the vibrational data at a first vacuum level.
- PCA Principal component analysis
- FIG. 15A through FIG. 15C illustrates graphical representations of vibration fault detection using transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components, respectively of the vibrational data at a second vacuum level.
- PCA Principal component analysis
- FIG. 16A through FIG. 16D illustrates an exemplary graphical representation of pressure state alarm, filter alarm, oil state alarm, and blower alarm when a bad oil is detected with respect to a silencer.
- FIG. 17A through FIG. 17D illustrates graphical representations of pressure state alarm, filter alarm, oil state alarm, and blower alarm, respectively, when a legacy pump has a clogged filter.
- FIG. 18 is a graphical representation of overfill alarm, according to one embodiment. Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description that follows.
- Example embodiments may be used to provide a method, an apparatus and/or a system of real time machine learning based predictive and preventive maintenance of a vacuum pump.
- FIG. 1 is a system diagram of a machine learning architecture, according to one embodiment.
- the machine learning architecture 100 may include a vacuum pump 106 associated with a blower 110 and motor 108 .
- the architecture 100 may include one or more machine wearable sensors 114 , 112 coupled respectively to the blower 110 and the motor 108 of the vacuum pump, a communications network 102 , and a mobile application 118 associated with a mobile device.
- the mobile application 118 may be communicatively coupled to the machine wearable sensors 112 , 114 over the communications network 102 .
- the machine learning architecture 100 may receive sensor data from the blower 110 and the motor 108 and classifies the motor sensor data into vacuum state sensor data and break state sensor data.
- the machine learning architecture 100 analyzes the vacuum state sensor data to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range.
- a computer database 116 in communication with the mobile application 118 through the communication network 102 and machine learning algorithm engine 104 .
- the machine learning architecture 100 classifies vacuum break data into clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected.
- the machine learning architecture 100 analyzes the blower sensor data in association with the motor sensor data through a machine learning algorithm in order to detect a deficient oil level and a deficient oil structure.
- the motor sensor data may be determined from a machine wearable sensor placed on the motor.
- the blower sensor data may also be determined from a machine wearable sensor placed on the blower.
- the communications network may include WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof.
- the machine learning architecture may be associated with a machine learning algorithm.
- the motor sensor data and blower sensor data may be received over a communications network onto a mobile application coupled to a mobile device.
- the alarm may be raised over the communications network through one of a notification on the mobile application including Short message service (SMS), email, or a combination thereof.
- SMS Short message service
- machine learning of a vibrational data may comprise of information related to shape factor of the vibration calculated as a ratio of moving RMS (Root mean square) value to moving average of absolute value.
- FIG. 2 is a diagrammatic representation of a data processing system capable of processing a set of instructions to perform any one or more of the methodologies herein.
- FIG. 2 shows a diagrammatic representation of a machine in the exemplary form of a computer system 200 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.
- the machine operates as a standalone device and/or may be connected (e.g., networked) to other machines.
- the machine may operate in the capacity of a server and/or as a client machine in server-client network environment, and or as a peer machine in a peer-to-peer (or distributed) network environment.
- the machine may be a personal-computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch and or bridge, an embedded system and/or any machine capable of executing a set of instructions (sequential and/or otherwise) that specify actions to be taken by that machine.
- PC personal-computer
- PDA Personal Digital Assistant
- STB set-top box
- STB set-top box
- PDA Personal Digital Assistant
- the exemplary computer system 200 includes a processor 202 (e.g., a central processing unit (CPU) a graphics processing unit (GPU) and/or both), a main memory 204 and a static memory 206 , which communicate with each other via a bus 208 .
- the computer system 200 may further include a video display unit 210 (e.g., a liquid crystal displays (LCD) and/or a cathode ray tube (CRT)).
- the computer system 200 also includes an alphanumeric input device 212 (e.g., a keyboard), a cursor control device 214 (e.g., a mouse), a disk drive unit 216 , a signal generation device 218 (e.g., a speaker) and a network interface device 220 .
- an alphanumeric input device 212 e.g., a keyboard
- a cursor control device 214 e.g., a mouse
- a disk drive unit 216 e.g., a disk drive unit 216
- a signal generation device 218 e.g., a speaker
- the disk drive unit 216 further includes a machine-readable medium 222 on which one or more sets of instructions 224 (e.g., software) embodying any one or more of the methodologies and/or functions described herein is stored.
- the instructions 224 may also reside, completely and/or at least partially, within the main memory 204 and/or within the processor 202 during execution thereof by the computer system 200 .
- the main memory 204 and the processor 202 also constituting machine-readable media.
- the instructions 224 may further be transmitted and/or received over a network 226 via the network interface device 220 .
- the machine-readable medium 222 is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium and/or multiple media (e.g., a centralized and/or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.
- the term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding and/or carrying a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the various embodiments.
- the term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals.
- FIG. 3 is a process flow diagram detailing the operations of one of a method of the machine learning architecture, according to one or more embodiments.
- the method of machine learning architecture includes: receiving a motor sensor data and a blower sensor data over a communications network 302 ; classifying the motor sensor data into one of a vacuum state sensor data and break state sensor data 304 ; and analyzing the vacuum state sensor data to detect an operating vacuum level 306 and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Further, the method includes classifying vacuum break data into clean filter category and clogged filter category 308 and an alarm is raised if an entry under the clogged filter category is detected. Also, the method includes analyzing the blower sensor data in association with the motor sensor data based on machine learning to detect deficient oil level and deficient oil structure 310 .
- the method of machine learning architecture may also include determining motor sensor data from machine wearable sensor placed on the motor and determining blower sensor data from machine wearable sensor placed on the blower.
- the communications network may include WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof.
- the machine learning architecture may be associated with a machine learning algorithm.
- the motor sensor data and blower sensor data may be received over a communications network onto a mobile application associated with a mobile device and an alarm may be raised over the communications network through one of a notification on the mobile application, Short message service (SMS), email, or a combination thereof.
- SMS Short message service
- the Internet of Things (IoT) based system may include machine wearable sensors. Further, the IoT system may be used for overseeing process control and predictive maintenance of a machine or a network of machines.
- the system may include a plurality of machine-wearable sensors, each of which secured to the exterior of the machine. Each sensor may be capable of transmitting captured data wirelessly over a communications network.
- the system may further include a sensor network for receiving and transmitting the captured data over a communications network and a machine learning algorithm engine capable of receiving data from the sensor network.
- the machine learning algorithm engine may process the received data to recognize one of a pattern and a deviation to issue control commands pertaining to the machine.
- the system may include one or more control modules disposed in operative communication with a control panel of the machine, wherein the control module is capable of receiving control commands over a communications network and executing the control commands.
- the machine learning algorithm engine may raise an alarm when one of a filter is clogged and deficient oil is detected, wherein the deficient oil may be one of a low oil level and an overused oil structure.
- the plurality of machine wearable sensors may include motor sensors and blower sensors.
- the machine learning algorithm engine associated with the IoT based system may issue commands based on a learning outcome from the motor sensor data and the blower sensor data. The learning outcome may be dependent on recognition of one of a pattern and deviation by the machine learning algorithm engine.
- the machine learning algorithm engine may include three layers which may be used for predictive and preventive maintenance of vacuum pump.
- the machine learning algorithm engine may deploy three layers of supervised machine learning for predictive and preventive maintenance.
- Layer one of the supervised machine learning may receive vibration data from motor and/or blower, the vibration data may be classified into vacuum state and vacuum break state.
- the vacuum break state may be a state in which vacuum is released periodically.
- a motor vibration data from vacuum state may be classified to detect operating vacuum level such as ⁇ 8 inch or ⁇ 12 inch of mercury. Then depending on a safety range (E.g.: between ⁇ 7 to ⁇ 12 inch of mercury), an alarm may be raised and conveyed to the users via a mobile application.
- operating vacuum level such as ⁇ 8 inch or ⁇ 12 inch of mercury.
- a safety range E.g.: between ⁇ 7 to ⁇ 12 inch of mercury
- vacuum break data may be classified into clean filter category and clogged filter category using a supervised machine learning. If the clog filter category is detected then an alarm may be raised.
- Blower Temperature and blower vibration data may be used for classification of a bad and/or low oil level.
- Bad oil level may increase the friction and thereby raise the surface temp of a blower.
- the machine learning based classification includes an oblique and/or support vector machine.
- Support vector machines may be supervised learning models with associated learning algorithms that analyze data and recognize patterns.
- the supervised learning models may be used for classification and regression analysis.
- Motor vibration data may not be affected by bad oil. However, blower vibration data may get affected by bad oil. Therefore motor vibration data may be indicative of a particular vacuum pressure level. By comparing the blower data for good and bad oil using supervised machine learning, operation with bad oil may be detected.
- FIG. 4 is an exemplary representation of data on a mobile application associated with the machine learning architecture, according to one or more embodiments.
- FIG. 4 shows a sensor data representation on a mobile application associated with a mobile device.
- FIG. 5 is an exemplary representation of a mobile status dashboard for a dryer.
- the mobile status dashboard displays a status of a process anytime, anywhere through a connection with an internet.
- FIG. 6 is an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device, generated automatically by a Dryer, according to one or more embodiments.
- FIG. 7 an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device, generated automatically by a Vacuum pump, according to one or more embodiments.
- FIG. 8 is a representation of a mobile application tracking abusive operations for preventive maintenance, according to one embodiment.
- FIG. 9 is a representation of a real time status of a vacuum pump as seen on a mobile application, according to one embodiment.
- FIG. 10 is a representation of Real time trend of drying-indicating clean dryer against dryers with clogged filter, according to one embodiment.
- FIG. 11 is a representation of normal oil levels against low oil levels, according to one embodiment.
- FIG. 12 is a clustering diagram showing pump Temperature and vibration indicating different possible cluster of operation based on oil levels, according to one embodiment.
- Vacuum pumps may report one or more of temperature, vibration, pressure and sound. These data may be used by a platform to check against a baseline pump database and the platform offers early warning for pump failure and/or real time alarm for abusive operation. Similarly, a blower temperature along with vibration may also be tracked. From machine learning algorithms of data, the platform sends out early indication of clogging of safety filters and/or low oil level indication.
- FIG. 13 is a representation of normalized vibration against time. Vibration data may help to identify clogged safety filter in a vacuum pump to stop abusive operation. Clogged safety filters may lead to malfunctioning of pump within months.
- a drying process with a check on health is available tracking temperature and flow data at inlet, outlet and on site glass of a dryer.
- a recorded database may be created for normal and/or baseline operation with a clean filter,
- a mobile application may indicate degradation of filters and drying process.
- the mobile application may also offer recommended operation for optimal temperature to save energy and may also act as a platform for dryer maintenance.
- a machine learning architecture may be associated with a machine learning algorithm where normal states of the vacuum pumps with operational range, clean filer and clean oil may be learned with a baseline reading. Further, anomalous readings from one of a clogged filter, a bad operation, a bad oil, a low oil level and an over filled oil level are also recorded. The baseline reading and the anomalous readings may be used as a training database for the machine learning algorithm.
- data from multiple vacuum pumps associated with machine wearable sensors may also be acquired.
- a mobile or web or desktop application may act as a mobile middleware to scale the machine learning architecture to a single data collection unit.
- the single data collection unit may be one of a mobile device and a wireless device.
- the machine learning may be used on a transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components of the vibrational data to transpose an acceleration into reference frame of the rotor of the vacuum pump.
- PCA Principal component analysis
- Machine learning of the vibrational data may comprise a transfer of vibrational energy from one axis of rotation to other axis in order to determine the extent of oldness of the oil used in the blower bearings for smooth rotation.
- Machine learning of the vibrational data may also comprise information related to instability and wobbling of rigid rotational axis which aids in determining an extent of oldness of oil used in bearings of the blower.
- a predictive and preventive maintenance system for a vacuum pump may include one or more machine wearable sensors associated with the vacuum pump, a tracking module associated with a computing device, a machine learning module associated with a database and a communications network.
- a changing condition of vacuum pump may be tracked through the tracking module over the communications network.
- the tracking module may receive one of a temperature, a vibration and a sound data from the one or more machine wearable sensors.
- the machine learning module associated with the tracking module may identify a pattern from the temperature, the sound and the vibration data and may raise an alarm based on an analysis of the pattern.
- FIG. 16A through FIG. 16D illustrates an exemplary graphical representation of pressure state alarm, filter alarm, oil state alarm, and blower alarm when a bad oil is detected with respect to a silencer.
- FIG. 17A through FIG. 17D illustrates graphical representations of pressure state alarm, filter alarm, oil state alarm, and blower alarm, respectively, when a legacy pump has a clogged filter.
- FIG. 18 is a graphical representation of overfill alarm, according to one embodiment.
- a wearable sensor may be one of a MEMS or a single silicon sensor.
- the various devices and modules described herein may be enabled and operated using hardware circuitry, firmware, software or any combination of hardware, firmware, and software (e.g., embodied in a machine readable medium).
- the various electrical structure and methods may be embodied using transistors, logic gates, and electrical circuits (e.g., application specific integrated (ASIC) circuitry and/or in Digital Signal Processor (DSP) circuitry).
- ASIC application specific integrated
- DSP Digital Signal Processor
- the various operations, processes, and methods disclosed herein may be embodied in a machine-readable medium and/or a machine accessible medium compatible with a data processing system (e.g., a computer devices), and may be performed in any order (e.g., including using means for achieving the various operations).
- the medium may be, for example, a memory, a transportable medium such as a CD, a DVD, a Blu-ray disc, a floppy disk, or a diskette.
- a computer program embodying the aspects of the exemplary embodiments may be loaded onto the retail portal.
- the computer program is not limited to specific embodiments discussed above, and may, for example, be implemented in an operating system, an application program, a foreground or background process, a driver, a network stack or any combination thereof.
- the computer program may be executed on a single computer processor or multiple computer processors.
Abstract
Description
- The present invention generally relates to Internet of Things (IoT), and more particularly relates to an IoT-based system for predictive and preventive maintenance of machines that uses a blower and a pump, through machine learning and physics based modeling of physical parameters like vibration, sound, temperature monitored by machine wearable and other related sensors.
- Internet of Things (IoT) is a network of uniquely-identifiable and purposed “things” that are enabled to communicate data over a communications network without requiring human-to-human or human-to-computer interaction. The “thing” in the Internet of Things may virtually be anything that fits into a common purpose thereof. For example, a “thing” could be a person with a heart rate monitor implant, a farm animal with a biochip transponder, an automobile comprising built-in sensors configured to alert its driver when the tire pressure is low, or the like, or any other natural or man-made entity that can be assigned with a unique IP address and provided with the ability to transfer data over a network. Notably, if all the entities in an IoT are machines, then the IoT is referred to as a Machine to Machine (M2M) IoT or simply, as M2M IoT.
- It is apparent from the aforementioned examples that an entity becomes a “thing” of an M2M IoT especially, when the entity is attached with one or more sensors capable of capturing one or more types of data pertaining to: segregation of the data (if applicable); selective communication of each segregation of data to one or more fellow “things”; reception of one or more control commands (or instructions) from one or more fellow “things” wherein, the control commands are based on the data received by the one or more fellow “things”; and execution of the commands resulting in manipulation or “management” of an operation of the corresponding entity. Therefore, in an IoT-enabled system, the “things” basically manage themselves without any human intervention, thus drastically improving the efficiency thereof.
- EP Patent No. 1836576 B1 discusses a diagnostic method of failure protection of vacuum pumps. Based on comparison of the currently evaluated diagnostics analysis results and an initial data, maintenance engineers would decide the replacement of the considered vacuum pump, according to the evaluated pump performance indicators. However, in this prior art invention, there is no mention of machine learning or use of machine wearable sensors. Also, the remedial decisions are left to the maintenance engineers.
- US Patent application 20120209569 A1 discusses a method for predicting a failure in rotation of a rotor of a vacuum pump. The prior art invention fails to disclose machine learning capabilities and also is dependent on an observation time prediction window. Further, the prior art fails to disclose machine wearable sensors and Internet of things.
- U.S. Pat. No. 7,882,394 B2 discusses fault diagnostics through a data collection module. The prior art discloses a system for condition monitoring and fault diagnosis that includes: a data collection function that acquires time histories of selected variables for one or more of components; a pre-processing function that calculates specified characteristics of time histories; an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the components, and a reasoning function for determining the condition of the components from one or more hypotheses. The prior art invention however, fails to suggest the concept of IoT. Further, the prior art invention does not mention machine learning for effective predictive or preventive maintenance of vacuum pumps or similar devices.
- It is evident from the discussion of the aforementioned prior art that none of them discloses or suggests regarding predictive and preventive maintenance of vacuum pumps through machine learning. Therefore, there is a need in the art for a solution to the aforementioned problem.
- A method of machine learning architecture according to the present invention includes a step of: receiving data from machine wearable sensors placed on a motor (henceforth motor sensor data) and a blower (henceforth a blower sensor data) over a communications network. The machine wearable sensors can be selected from a group consisting of vibration sensors, temperature sensors, magnetic field sensors, gyroscope and its combinations thereof. The sensor type can be single silicon or MEMS (Micro-electromechanical systems) type. The motor or blower sensor data is classified into one of a vacuum state sensor data and vacuum break state (where rotor is switched off and the revolution of the rotor is gradually damping in vacuum medium) sensor data, wherein the vacuum state sensor data is further analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. The vacuum break state sensor data is then classified into clean filter category and clogged filter category and an alarm is raised if the real time data of sensors belonging to clogged filter category is detected. Vacuum state data is further classified based on a multi-class learning model which classifies a pump running with machine oil into clean, old, leaked or overfilled classes. If the sensor data suggest neither, it is classified under uncategorized bearing issues. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect deficient oil level and deficient oil structure.
- An IoT based machine learning architecture according to an embodiment of the present invention includes: a vacuum pump associated with a blower and a motor coupled with one or more machine wearable sensors; a communications network; and a mobile application associated with a mobile device. The mobile application is communicatively coupled to one or more machine wearable sensors, over the communications network. The mobile application can be replaced by a PC based communication such as a PC based app, as well. The machine learning architecture receives sensor data from the blower and the motor and classifies the motor sensor data into vacuum state sensor data and break state sensor data. Also, the machine learning architecture analyzes the vacuum state sensor data to detect an operating vacuum level and an alarm is_raised when the vacuum state sensor data exceeds a pre-defined safety range. The machine learning architecture classifies vacuum break data into clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected, and the machine learning architecture further analyzes the blower sensor data in association with the motor sensor data through a machine learning algorithm in order to detect at least one of a deficient oil level and a deficient oil structure.
- The present invention relates to an Internet of Things (IoT) based system for overseeing process control and predictive maintenance of a machine or a network of machines by employing machine wearable sensors. The IoT based system includes a plurality of machine-wearable sensors, secured to the exterior of the machine. These sensors can be any combination of Temperature sensors, Vibration sensors, Magnetometer, Gyroscope. Each sensor is capable of transmitting captured data wirelessly over a communications network. The IoT based system further includes a sensor network for receiving and transmitting the captured data over a communications network. The system also includes: a machine learning algorithm engine capable of receiving data from the sensor network and processing the received data to recognize one of a pattern and a deviation to issue an alarm and appropriate control commands pertaining to the machine. The system further includes one or more control modules disposed in operative communication with the control panel of the machine, wherein the control module is capable of receiving the control commands over a communications network and executing the control commands.
- The methods and systems disclosed herein may be implemented in any means for achieving various aspects, and may be executed in a form of a machine-readable medium embodying a set of instructions that, when executed by a machine, cause the machine to perform any of the operations disclosed herein. Other features will be apparent from the accompanying drawings and from the detailed description that follows.
- The embodiments of this invention are illustrated in a non-limiting but in a way of example, in the figures of the accompanying drawings, in which like references indicate similar elements and in which:
-
FIG. 1 is a diagrammatic representation of a machine learning architecture, according to one or more embodiments. -
FIG. 2 is a diagrammatic representation of a data processing system capable of processing a set of instructions to perform any one or more of the methodologies herein, according to one embodiment. -
FIG. 3 is a process flow diagram detailing the operations of a method of a machine learning architecture, according to one or more embodiments. -
FIG. 4 is an exemplary representation of data on a mobile application associated with the machine learning architecture, according to one or more embodiments. -
FIG. 5 is an exemplary representation of a mobile status dashboard for a dryer. The mobile status dashboard displays a status of a process anytime, anywhere through a connection with an internet. -
FIG. 6 is an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device generated automatically by a Dryer, according to one or more embodiments. -
FIG. 7 is an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device generated automatically by a Vacuum pump, according to one or more embodiments. -
FIG. 8 is a representation of a mobile application tracking abusive operations for preventive maintenance so that pumps may last longer from real time vibration data, according to one embodiment. -
FIG. 9 is a representation of a real time status of a vacuum pump as seen on a mobile application, according to one embodiments. -
FIG. 10 is a representation of real time trend of drying-indicating clean dryer against dryers with clogged filter, according to one embodiment. -
FIG. 11 is a representation of normal oil levels against low oil levels, according to one embodiment. -
FIG. 12 is a clustering diagram showing pump temperature and vibration indicating different possible cluster of operation based on oil levels, according to one embodiment. -
FIG. 13 is a representation of normalized vibration against time, according to one embodiment. -
FIG. 14A throughFIG. 14C illustrates graphical representations of vibration fault detection using transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components, respectively of the vibrational data at a first vacuum level. -
FIG. 15A throughFIG. 15C illustrates graphical representations of vibration fault detection using transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components, respectively of the vibrational data at a second vacuum level. -
FIG. 16A throughFIG. 16D illustrates an exemplary graphical representation of pressure state alarm, filter alarm, oil state alarm, and blower alarm when a bad oil is detected with respect to a silencer. -
FIG. 17A throughFIG. 17D illustrates graphical representations of pressure state alarm, filter alarm, oil state alarm, and blower alarm, respectively, when a legacy pump has a clogged filter. -
FIG. 18 is a graphical representation of overfill alarm, according to one embodiment. Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description that follows. - Example embodiments, as described below, may be used to provide a method, an apparatus and/or a system of real time machine learning based predictive and preventive maintenance of a vacuum pump. Although the present embodiments have been described with reference to specific exemplary embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention.
-
FIG. 1 is a system diagram of a machine learning architecture, according to one embodiment. Themachine learning architecture 100 may include avacuum pump 106 associated with ablower 110 andmotor 108. Thearchitecture 100 may include one or more machinewearable sensors blower 110 and themotor 108 of the vacuum pump, acommunications network 102, and amobile application 118 associated with a mobile device. Themobile application 118 may be communicatively coupled to the machinewearable sensors communications network 102. Themachine learning architecture 100 may receive sensor data from theblower 110 and themotor 108 and classifies the motor sensor data into vacuum state sensor data and break state sensor data. Themachine learning architecture 100 analyzes the vacuum state sensor data to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Acomputer database 116 in communication with themobile application 118 through thecommunication network 102 and machinelearning algorithm engine 104. Themachine learning architecture 100 classifies vacuum break data into clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. Themachine learning architecture 100 analyzes the blower sensor data in association with the motor sensor data through a machine learning algorithm in order to detect a deficient oil level and a deficient oil structure. - In one or more embodiments, the motor sensor data may be determined from a machine wearable sensor placed on the motor. Similarly, the blower sensor data may also be determined from a machine wearable sensor placed on the blower. The communications network may include WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof.
- In one or more embodiments, the machine learning architecture may be associated with a machine learning algorithm. The motor sensor data and blower sensor data may be received over a communications network onto a mobile application coupled to a mobile device. The alarm may be raised over the communications network through one of a notification on the mobile application including Short message service (SMS), email, or a combination thereof.
- In one or more embodiments, machine learning of a vibrational data may comprise of information related to shape factor of the vibration calculated as a ratio of moving RMS (Root mean square) value to moving average of absolute value.
-
FIG. 2 is a diagrammatic representation of a data processing system capable of processing a set of instructions to perform any one or more of the methodologies herein.FIG. 2 shows a diagrammatic representation of a machine in the exemplary form of acomputer system 200 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In various embodiments, the machine operates as a standalone device and/or may be connected (e.g., networked) to other machines. - In a networked deployment, the machine may operate in the capacity of a server and/or as a client machine in server-client network environment, and or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal-computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch and or bridge, an embedded system and/or any machine capable of executing a set of instructions (sequential and/or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually and/or jointly execute a set (or multiple sets) of instructions to perform any one and/or more of the methodologies discussed herein.
- The
exemplary computer system 200 includes a processor 202 (e.g., a central processing unit (CPU) a graphics processing unit (GPU) and/or both), amain memory 204 and astatic memory 206, which communicate with each other via abus 208. Thecomputer system 200 may further include a video display unit 210 (e.g., a liquid crystal displays (LCD) and/or a cathode ray tube (CRT)). Thecomputer system 200 also includes an alphanumeric input device 212 (e.g., a keyboard), a cursor control device 214 (e.g., a mouse), adisk drive unit 216, a signal generation device 218 (e.g., a speaker) and anetwork interface device 220. - The
disk drive unit 216 further includes a machine-readable medium 222 on which one or more sets of instructions 224 (e.g., software) embodying any one or more of the methodologies and/or functions described herein is stored. Theinstructions 224 may also reside, completely and/or at least partially, within themain memory 204 and/or within theprocessor 202 during execution thereof by thecomputer system 200. Themain memory 204 and theprocessor 202 also constituting machine-readable media. - The
instructions 224 may further be transmitted and/or received over anetwork 226 via thenetwork interface device 220. While the machine-readable medium 222 is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium and/or multiple media (e.g., a centralized and/or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding and/or carrying a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the various embodiments. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. -
FIG. 3 is a process flow diagram detailing the operations of one of a method of the machine learning architecture, according to one or more embodiments. In one embodiment, the method of machine learning architecture includes: receiving a motor sensor data and a blower sensor data over acommunications network 302; classifying the motor sensor data into one of a vacuum state sensor data and breakstate sensor data 304; and analyzing the vacuum state sensor data to detect an operatingvacuum level 306 and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Further, the method includes classifying vacuum break data into clean filter category and cloggedfilter category 308 and an alarm is raised if an entry under the clogged filter category is detected. Also, the method includes analyzing the blower sensor data in association with the motor sensor data based on machine learning to detect deficient oil level anddeficient oil structure 310. - In one or more embodiments, the method of machine learning architecture may also include determining motor sensor data from machine wearable sensor placed on the motor and determining blower sensor data from machine wearable sensor placed on the blower.
- In one or more embodiments, the communications network may include WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof. The machine learning architecture may be associated with a machine learning algorithm.
- In one or more embodiments, the motor sensor data and blower sensor data may be received over a communications network onto a mobile application associated with a mobile device and an alarm may be raised over the communications network through one of a notification on the mobile application, Short message service (SMS), email, or a combination thereof.
- In an exemplary embodiment, the Internet of Things (IoT) based system may include machine wearable sensors. Further, the IoT system may be used for overseeing process control and predictive maintenance of a machine or a network of machines. The system may include a plurality of machine-wearable sensors, each of which secured to the exterior of the machine. Each sensor may be capable of transmitting captured data wirelessly over a communications network. The system may further include a sensor network for receiving and transmitting the captured data over a communications network and a machine learning algorithm engine capable of receiving data from the sensor network. The machine learning algorithm engine may process the received data to recognize one of a pattern and a deviation to issue control commands pertaining to the machine. Lastly, the system may include one or more control modules disposed in operative communication with a control panel of the machine, wherein the control module is capable of receiving control commands over a communications network and executing the control commands.
- In an exemplary embodiment, the machine learning algorithm engine may raise an alarm when one of a filter is clogged and deficient oil is detected, wherein the deficient oil may be one of a low oil level and an overused oil structure. The plurality of machine wearable sensors may include motor sensors and blower sensors. The machine learning algorithm engine associated with the IoT based system may issue commands based on a learning outcome from the motor sensor data and the blower sensor data. The learning outcome may be dependent on recognition of one of a pattern and deviation by the machine learning algorithm engine.
- In an exemplary embodiment, the machine learning algorithm engine may include three layers which may be used for predictive and preventive maintenance of vacuum pump.
- The machine learning algorithm engine may deploy three layers of supervised machine learning for predictive and preventive maintenance. Layer one of the supervised machine learning may receive vibration data from motor and/or blower, the vibration data may be classified into vacuum state and vacuum break state. In one or more embodiments, the vacuum break state may be a state in which vacuum is released periodically.
- In layer two, a motor vibration data from vacuum state may be classified to detect operating vacuum level such as −8 inch or −12 inch of mercury. Then depending on a safety range (E.g.: between −7 to −12 inch of mercury), an alarm may be raised and conveyed to the users via a mobile application.
- In layer three, vacuum break data may be classified into clean filter category and clogged filter category using a supervised machine learning. If the clog filter category is detected then an alarm may be raised.
- Blower Temperature and blower vibration data may be used for classification of a bad and/or low oil level. Bad oil level may increase the friction and thereby raise the surface temp of a blower. The machine learning based classification includes an oblique and/or support vector machine. Support vector machines may be supervised learning models with associated learning algorithms that analyze data and recognize patterns. The supervised learning models may be used for classification and regression analysis.
- Motor vibration data may not be affected by bad oil. However, blower vibration data may get affected by bad oil. Therefore motor vibration data may be indicative of a particular vacuum pressure level. By comparing the blower data for good and bad oil using supervised machine learning, operation with bad oil may be detected.
-
FIG. 4 is an exemplary representation of data on a mobile application associated with the machine learning architecture, according to one or more embodiments.FIG. 4 shows a sensor data representation on a mobile application associated with a mobile device. -
FIG. 5 is an exemplary representation of a mobile status dashboard for a dryer. The mobile status dashboard displays a status of a process anytime, anywhere through a connection with an internet. -
FIG. 6 is an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device, generated automatically by a Dryer, according to one or more embodiments. -
FIG. 7 an exemplary representation of historical records of alarms/issues any time as displayed on a Mobile application associated with a mobile device, generated automatically by a Vacuum pump, according to one or more embodiments. -
FIG. 8 is a representation of a mobile application tracking abusive operations for preventive maintenance, according to one embodiment. -
FIG. 9 is a representation of a real time status of a vacuum pump as seen on a mobile application, according to one embodiment. -
FIG. 10 is a representation of Real time trend of drying-indicating clean dryer against dryers with clogged filter, according to one embodiment. -
FIG. 11 is a representation of normal oil levels against low oil levels, according to one embodiment. -
FIG. 12 is a clustering diagram showing pump Temperature and vibration indicating different possible cluster of operation based on oil levels, according to one embodiment. - Pumps may run into failure very often due to abusive operation coupled with poor maintenance. Vacuum pumps may report one or more of temperature, vibration, pressure and sound. These data may be used by a platform to check against a baseline pump database and the platform offers early warning for pump failure and/or real time alarm for abusive operation. Similarly, a blower temperature along with vibration may also be tracked. From machine learning algorithms of data, the platform sends out early indication of clogging of safety filters and/or low oil level indication.
-
FIG. 13 is a representation of normalized vibration against time. Vibration data may help to identify clogged safety filter in a vacuum pump to stop abusive operation. Clogged safety filters may lead to malfunctioning of pump within months. - In an exemplary embodiment, a drying process with a check on health is available tracking temperature and flow data at inlet, outlet and on site glass of a dryer. A recorded database may be created for normal and/or baseline operation with a clean filter, By comparing with the baseline operation, a mobile application may indicate degradation of filters and drying process. The mobile application may also offer recommended operation for optimal temperature to save energy and may also act as a platform for dryer maintenance.
- In one or more embodiments, a machine learning architecture may be associated with a machine learning algorithm where normal states of the vacuum pumps with operational range, clean filer and clean oil may be learned with a baseline reading. Further, anomalous readings from one of a clogged filter, a bad operation, a bad oil, a low oil level and an over filled oil level are also recorded. The baseline reading and the anomalous readings may be used as a training database for the machine learning algorithm.
- In one or more embodiments, data from multiple vacuum pumps associated with machine wearable sensors may also be acquired. A mobile or web or desktop application may act as a mobile middleware to scale the machine learning architecture to a single data collection unit. The single data collection unit may be one of a mobile device and a wireless device.
- In one or more embodiments, the machine learning may be used on a transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components of the vibrational data to transpose an acceleration into reference frame of the rotor of the vacuum pump.
FIG. 14A throughFIG. 14C illustrates graphical representations of vibration fault detection using transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components, respectively of the vibrational data at a first vacuum level (Vacuum=8). -
FIG. 15A throughFIG. 15C illustrates graphical representations of vibration fault detection using transformed vibrational data based on PCA (Principal component analysis) transformation of X, Y and Z axis components, respectively of the vibrational data at a second vacuum level (Vacuum=6). - Machine learning of the vibrational data may comprise a transfer of vibrational energy from one axis of rotation to other axis in order to determine the extent of oldness of the oil used in the blower bearings for smooth rotation. Machine learning of the vibrational data may also comprise information related to instability and wobbling of rigid rotational axis which aids in determining an extent of oldness of oil used in bearings of the blower.
- In one or more embodiments, a predictive and preventive maintenance system for a vacuum pump may include one or more machine wearable sensors associated with the vacuum pump, a tracking module associated with a computing device, a machine learning module associated with a database and a communications network. A changing condition of vacuum pump may be tracked through the tracking module over the communications network. The tracking module may receive one of a temperature, a vibration and a sound data from the one or more machine wearable sensors. The machine learning module associated with the tracking module may identify a pattern from the temperature, the sound and the vibration data and may raise an alarm based on an analysis of the pattern.
-
FIG. 16A throughFIG. 16D illustrates an exemplary graphical representation of pressure state alarm, filter alarm, oil state alarm, and blower alarm when a bad oil is detected with respect to a silencer.FIG. 17A throughFIG. 17D illustrates graphical representations of pressure state alarm, filter alarm, oil state alarm, and blower alarm, respectively, when a legacy pump has a clogged filter.FIG. 18 is a graphical representation of overfill alarm, according to one embodiment. - In one or more embodiments, a wearable sensor may be one of a MEMS or a single silicon sensor.
- Although the present embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the various embodiments. For example, the various devices and modules described herein may be enabled and operated using hardware circuitry, firmware, software or any combination of hardware, firmware, and software (e.g., embodied in a machine readable medium). For example, the various electrical structure and methods may be embodied using transistors, logic gates, and electrical circuits (e.g., application specific integrated (ASIC) circuitry and/or in Digital Signal Processor (DSP) circuitry).
- In addition, it will be appreciated that the various operations, processes, and methods disclosed herein may be embodied in a machine-readable medium and/or a machine accessible medium compatible with a data processing system (e.g., a computer devices), and may be performed in any order (e.g., including using means for achieving the various operations). The medium may be, for example, a memory, a transportable medium such as a CD, a DVD, a Blu-ray disc, a floppy disk, or a diskette. A computer program embodying the aspects of the exemplary embodiments may be loaded onto the retail portal. The computer program is not limited to specific embodiments discussed above, and may, for example, be implemented in an operating system, an application program, a foreground or background process, a driver, a network stack or any combination thereof. The computer program may be executed on a single computer processor or multiple computer processors.
- Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.
Claims (17)
Priority Applications (11)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/628,322 US20160245279A1 (en) | 2015-02-23 | 2015-02-23 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
US14/696,402 US10599982B2 (en) | 2015-02-23 | 2015-04-25 | Internet of things based determination of machine reliability and automated maintainenace, repair and operation (MRO) logs |
US14/977,675 US20160245686A1 (en) | 2015-02-23 | 2015-12-22 | Fault detection in rotor driven equipment using rotational invariant transform of sub-sampled 3-axis vibrational data |
PCT/US2016/018820 WO2016137848A1 (en) | 2015-02-23 | 2016-02-20 | Internet of things based determination of machine reliability and automated maintainenace, repair and operation (mro) logs |
PCT/US2016/018831 WO2016137849A2 (en) | 2015-02-23 | 2016-02-21 | Fault detection in rotor driven equipment using rotational invariant transform of sub-sampled 3-axis vibrational data |
US16/253,925 US10598520B2 (en) | 2015-02-23 | 2019-01-22 | Method and apparatus for pneumatically conveying particulate material including a user-visible IoT-based classification and predictive maintenance system noting maintenance state as being acceptable, cautionary, or dangerous |
US16/253,462 US11002269B2 (en) | 2015-02-23 | 2019-01-22 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
US16/286,058 US10638295B2 (en) | 2015-01-17 | 2019-02-26 | System and method for turbomachinery preventive maintenance and root cause failure determination |
US16/439,875 US11162837B2 (en) | 2015-02-23 | 2019-06-13 | Detecting faults in rotor driven equipment |
US16/686,511 US20200081054A1 (en) | 2015-02-23 | 2019-11-18 | Power line issue diagnostic methods and apparatus using distributed analytics |
US16/826,764 US11092466B2 (en) | 2015-02-23 | 2020-03-23 | Internet of things based conveyance having predictive maintenance |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/628,322 US20160245279A1 (en) | 2015-02-23 | 2015-02-23 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
Related Parent Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/833,111 Continuation-In-Part US10648735B2 (en) | 2015-01-17 | 2015-08-23 | Machine learning based predictive maintenance of a dryer |
US16/253,462 Continuation-In-Part US11002269B2 (en) | 2015-01-17 | 2019-01-22 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
Related Child Applications (3)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/696,402 Continuation-In-Part US10599982B2 (en) | 2015-01-17 | 2015-04-25 | Internet of things based determination of machine reliability and automated maintainenace, repair and operation (MRO) logs |
US14/977,675 Continuation-In-Part US20160245686A1 (en) | 2015-01-17 | 2015-12-22 | Fault detection in rotor driven equipment using rotational invariant transform of sub-sampled 3-axis vibrational data |
US16/253,462 Continuation US11002269B2 (en) | 2015-01-17 | 2019-01-22 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
Publications (1)
Publication Number | Publication Date |
---|---|
US20160245279A1 true US20160245279A1 (en) | 2016-08-25 |
Family
ID=56689813
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/628,322 Abandoned US20160245279A1 (en) | 2015-01-17 | 2015-02-23 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
US16/253,462 Active US11002269B2 (en) | 2015-01-17 | 2019-01-22 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/253,462 Active US11002269B2 (en) | 2015-01-17 | 2019-01-22 | Real time machine learning based predictive and preventive maintenance of vacuum pump |
Country Status (1)
Country | Link |
---|---|
US (2) | US20160245279A1 (en) |
Cited By (36)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106640690A (en) * | 2016-12-28 | 2017-05-10 | 上海市城市排水有限公司机修安装分公司 | Real-time long-range monitoring system for drainage pump and real-time long-range monitoring method of real-time long-range monitoring system |
CN107013449A (en) * | 2017-04-18 | 2017-08-04 | 山东万腾电子科技有限公司 | Voice signal based on deep learning recognizes the method and system of compressor fault |
US9826338B2 (en) | 2014-11-18 | 2017-11-21 | Prophecy Sensorlytics Llc | IoT-enabled process control and predective maintenance using machine wearables |
US9823289B2 (en) | 2015-06-01 | 2017-11-21 | Prophecy Sensorlytics Llc | Automated digital earth fault system |
CN108757502A (en) * | 2018-05-15 | 2018-11-06 | 江苏大学 | A kind of water pump assembly typical case's health status monitoring device and method based on Internet of Things |
US10271115B2 (en) * | 2015-04-08 | 2019-04-23 | Itt Manufacturing Enterprises Llc. | Nodal dynamic data acquisition and dissemination |
CN109882401A (en) * | 2019-04-15 | 2019-06-14 | 湖南中普新能源科技有限公司 | Air compressor machine self-test strategy based on Internet of Things |
WO2019182894A1 (en) * | 2018-03-19 | 2019-09-26 | Ge Inspection Technologies, Lp | Diagnosing and predicting electrical pump operation |
US10481195B2 (en) | 2015-12-02 | 2019-11-19 | Machinesense, Llc | Distributed IoT based sensor analytics for power line diagnosis |
EP3575909A1 (en) * | 2018-06-01 | 2019-12-04 | Siemens Aktiengesellschaft | Method for monitoring a mechanical system |
CN110705133A (en) * | 2019-11-06 | 2020-01-17 | 中国联合网络通信集团有限公司 | Predictive maintenance method and predictive maintenance equipment |
US10598520B2 (en) | 2015-02-23 | 2020-03-24 | Machinesense, Llc | Method and apparatus for pneumatically conveying particulate material including a user-visible IoT-based classification and predictive maintenance system noting maintenance state as being acceptable, cautionary, or dangerous |
US10599982B2 (en) | 2015-02-23 | 2020-03-24 | Machinesense, Llc | Internet of things based determination of machine reliability and automated maintainenace, repair and operation (MRO) logs |
US10613046B2 (en) | 2015-02-23 | 2020-04-07 | Machinesense, Llc | Method for accurately measuring real-time dew-point value and total moisture content of a material |
US10638295B2 (en) | 2015-01-17 | 2020-04-28 | Machinesense, Llc | System and method for turbomachinery preventive maintenance and root cause failure determination |
US10648735B2 (en) | 2015-08-23 | 2020-05-12 | Machinesense, Llc | Machine learning based predictive maintenance of a dryer |
US10650621B1 (en) | 2016-09-13 | 2020-05-12 | Iocurrents, Inc. | Interfacing with a vehicular controller area network |
US10711788B2 (en) | 2015-12-17 | 2020-07-14 | Wayne/Scott Fetzer Company | Integrated sump pump controller with status notifications |
USD890211S1 (en) | 2018-01-11 | 2020-07-14 | Wayne/Scott Fetzer Company | Pump components |
USD893552S1 (en) | 2017-06-21 | 2020-08-18 | Wayne/Scott Fetzer Company | Pump components |
US10745263B2 (en) * | 2015-05-28 | 2020-08-18 | Sonicu, Llc | Container fill level indication system using a machine learning algorithm |
CN111706499A (en) * | 2020-06-09 | 2020-09-25 | 成都数之联科技有限公司 | Predictive maintenance system and method for vacuum pump and automatic vacuum pump purchasing system |
US10789785B2 (en) * | 2018-06-11 | 2020-09-29 | Honeywell International Inc. | Systems and methods for data collection from maintenance-prone vehicle components |
US10810501B1 (en) * | 2017-10-20 | 2020-10-20 | Amazon Technologies, Inc. | Automated pre-flight and in-flight testing of aerial vehicles by machine learning |
US10839506B1 (en) | 2018-01-02 | 2020-11-17 | Amazon Technologies, Inc. | Detecting surface flaws using computer vision |
US10913549B1 (en) | 2016-03-28 | 2021-02-09 | Amazon Technologies, Inc. | Automated aerial vehicle inspections |
US10921792B2 (en) | 2017-12-21 | 2021-02-16 | Machinesense Llc | Edge cloud-based resin material drying system and method |
US10921777B2 (en) | 2018-02-15 | 2021-02-16 | Online Development, Inc. | Automated machine analysis |
CN112628132A (en) * | 2020-12-24 | 2021-04-09 | 上海大学 | Water pump key index prediction method based on machine learning |
US11002269B2 (en) | 2015-02-23 | 2021-05-11 | Machinesense, Llc | Real time machine learning based predictive and preventive maintenance of vacuum pump |
CN113127473A (en) * | 2021-05-18 | 2021-07-16 | 浙江太美医疗科技股份有限公司 | Method, system and computer readable medium for processing medical data |
US11097856B1 (en) | 2019-02-18 | 2021-08-24 | Amazon Technologies, Inc. | Determining integrity of acoustically excited objects |
US11162837B2 (en) | 2015-02-23 | 2021-11-02 | Machinesense, Llc | Detecting faults in rotor driven equipment |
CN113688011A (en) * | 2021-08-26 | 2021-11-23 | 广东鑫钻节能科技股份有限公司 | Screw blower gas station control system based on Internet of things |
US11263876B2 (en) * | 2017-09-28 | 2022-03-01 | Ncr Corporation | Self-service terminal (SST) maintenance and support processing |
CN115076089A (en) * | 2022-07-06 | 2022-09-20 | 中南大学 | Hydraulic pump flow distribution pair oil film characteristic online test device based on end cover transformation |
Families Citing this family (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111287953A (en) * | 2020-02-24 | 2020-06-16 | 山东泰展机电科技股份有限公司 | Passenger car air pump performance test system and test method |
US11604456B2 (en) | 2020-03-11 | 2023-03-14 | Ford Global Technologies, Llc | System for monitoring machining processes of a computer numerical control machine |
US11620895B2 (en) * | 2020-08-05 | 2023-04-04 | Allstate Insurance Company | Systems and methods for disturbance detection and identification based on disturbance analysis |
CN113627660B (en) * | 2021-07-30 | 2023-11-10 | 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 | Method and device for predicting productivity of ceramic filter |
US11939862B2 (en) | 2021-09-27 | 2024-03-26 | Halliburton Energy Services, Inc. | Cementing unit power on self test |
US11643908B1 (en) | 2021-11-04 | 2023-05-09 | Halliburton Energy Services, Inc. | Automated configuration of pumping equipment |
US11852134B2 (en) | 2021-11-04 | 2023-12-26 | Halliburton Energy Services, Inc. | Automated mix water test |
DE102021213084A1 (en) | 2021-11-22 | 2022-12-15 | Carl Zeiss Smt Gmbh | Method for operating an EUV reflectometer |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080109185A1 (en) * | 2004-12-17 | 2008-05-08 | Korea Research Institute Of Standsards And Science | Precision Diagnostic Method For The Failure Protection And Predictive Maintenance Of A Vacuum Pump And A Precision Diagnostic System Therefor |
US20130201316A1 (en) * | 2012-01-09 | 2013-08-08 | May Patents Ltd. | System and method for server based control |
US20150139817A1 (en) * | 2013-11-19 | 2015-05-21 | Gardner Denver Thomas, Inc. | Ramp-up optimizing vacuum system |
Family Cites Families (134)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4023940A (en) | 1975-07-02 | 1977-05-17 | Whitlock, Inc. | Regeneration cycle control for industrial air dryer |
US4131011A (en) | 1977-02-28 | 1978-12-26 | Abbott Laboratories | Method and device for determining the end point for drying |
US7164117B2 (en) | 1992-05-05 | 2007-01-16 | Automotive Technologies International, Inc. | Vehicular restraint system control system and method using multiple optical imagers |
US5150289A (en) | 1990-07-30 | 1992-09-22 | The Foxboro Company | Method and apparatus for process control |
JPH06511212A (en) | 1991-05-09 | 1994-12-15 | ヌ−テック アンド エンジニアリング インコーポレイテッド | Instrument display methods and systems for passenger cars |
US5610339A (en) | 1994-10-20 | 1997-03-11 | Ingersoll-Rand Company | Method for collecting machine vibration data |
US5487225A (en) | 1994-11-14 | 1996-01-30 | The Conair Group, Inc. | Apparatus and method for controlled drying of plastic pellets |
US5995561A (en) | 1996-04-10 | 1999-11-30 | Silicon Systems, Inc. | Method and apparatus for reducing noise correlation in a partial response channel |
US6289606B2 (en) | 1997-06-13 | 2001-09-18 | Novatec, Inc. | Apparatus and method for moisture control of particulate material |
US6104987A (en) | 1997-10-03 | 2000-08-15 | The Nash Engineering Company | System for monitoring dryer drum temperatures |
US6260004B1 (en) * | 1997-12-31 | 2001-07-10 | Innovation Management Group, Inc. | Method and apparatus for diagnosing a pump system |
US6405108B1 (en) | 1999-10-28 | 2002-06-11 | General Electric Company | Process and system for developing predictive diagnostics algorithms in a machine |
US6437686B2 (en) | 2000-01-27 | 2002-08-20 | Kabushiki Kaisha Toshiba | System for monitoring airport equipments utilizing power-line carrier |
US6738748B2 (en) | 2001-04-03 | 2004-05-18 | Accenture Llp | Performing predictive maintenance on equipment |
US20050049834A1 (en) * | 2001-02-27 | 2005-03-03 | Bottomfield Roger L. | Non-invasive system and method for diagnosing potential malfunctions of semiconductor equipment components |
DE10125058B4 (en) | 2001-05-22 | 2014-02-27 | Enocean Gmbh | Thermally fed transmitter and sensor system |
US7797062B2 (en) | 2001-08-10 | 2010-09-14 | Rockwell Automation Technologies, Inc. | System and method for dynamic multi-objective optimization of machine selection, integration and utilization |
US20040199573A1 (en) | 2002-10-31 | 2004-10-07 | Predictive Systems Engineering, Ltd. | System and method for remote diagnosis of distributed objects |
US7016742B2 (en) | 2002-11-27 | 2006-03-21 | Bahelle Memorial Institute | Decision support for operations and maintenance (DSOM) system |
US6845340B2 (en) | 2003-03-06 | 2005-01-18 | Ford Motor Company | System and method for machining data management |
US20040176929A1 (en) | 2003-03-07 | 2004-09-09 | Dirk Joubert | Monitoring and maintaining equipment and machinery |
US7406399B2 (en) | 2003-08-26 | 2008-07-29 | Siemens Energy & Automation, Inc. | System and method for distributed reporting of machine performance |
US10227063B2 (en) | 2004-02-26 | 2019-03-12 | Geelux Holdings, Ltd. | Method and apparatus for biological evaluation |
FI119301B (en) | 2004-02-27 | 2008-09-30 | Metso Paper Inc | Method and system for the maintenance of papermaking machinery, processes, automation systems and equipment |
WO2005101281A2 (en) | 2004-04-06 | 2005-10-27 | Tyco Flow Control, Inc. | Field replaceable sensor module and methods of use thereof |
EP1657341A3 (en) | 2004-11-12 | 2006-08-23 | LG Electronics Inc. | Method and apparatus for control of drying process in a washing and drying machine |
PL1820034T3 (en) | 2004-11-18 | 2010-03-31 | Powersense As | Compensation of simple fiberoptic faraday effect sensors |
US20060168195A1 (en) | 2004-12-15 | 2006-07-27 | Rockwell Automation Technologies, Inc. | Distributed intelligent diagnostic scheme |
DE102005023256A1 (en) | 2005-05-20 | 2006-11-23 | Deere & Company, Moline | Monitoring device and a method for monitoring the function of the components of an agricultural machine |
WO2007008940A2 (en) | 2005-07-11 | 2007-01-18 | Brooks Automation, Inc. | Intelligent condition-monitoring and dault diagnostic system |
US20070100518A1 (en) | 2005-10-31 | 2007-05-03 | Cooper Johnny G | Method and System For Fluid Condition Monitoring |
US20070193056A1 (en) | 2006-02-21 | 2007-08-23 | Marius Switalski | Dryer assembly |
EP1884787A1 (en) | 2006-07-10 | 2008-02-06 | S. THIIM ApS | A current sensor for measuring electric current in a conductor and a short circuit indicator system comprising such a sensor |
US7558703B2 (en) | 2006-11-01 | 2009-07-07 | Abb Research Ltd. | Electrical substation monitoring and diagnostics |
TW200902558A (en) | 2007-02-16 | 2009-01-16 | Univation Tech Llc | Method for on-line monitoring and control of polymerization processes and reactors to prevent discontinuity events |
US10157422B2 (en) | 2007-05-10 | 2018-12-18 | Allstate Insurance Company | Road segment safety rating |
US20080289045A1 (en) | 2007-05-17 | 2008-11-20 | Thomas Michael Fryer | Method and device for encoding software to prevent reverse engineering, tampering or modifying software code, and masking the logical function of software execution |
KR100885919B1 (en) * | 2007-05-21 | 2009-02-26 | 삼성전자주식회사 | Pump fault prediction device and punp fault prediction method |
US8782182B2 (en) | 2007-05-24 | 2014-07-15 | Foundry Networks, Llc | Generating device-specific configurations |
WO2008148075A1 (en) | 2007-05-24 | 2008-12-04 | Alexander George Parlos | Machine condition assessment through power distribution networks |
ITVR20070074A1 (en) | 2007-05-25 | 2008-11-26 | Moretto Spa | PLANT AND DEHUMIDIFICATION PROCEDURE WITH VARIABLE REACH FOR GRANULAR MATERIALS |
US20090024359A1 (en) | 2007-07-16 | 2009-01-22 | Rockwell Automation Technologies, Inc. | Portable modular industrial data collector and analyzer system |
KR100874870B1 (en) | 2007-08-09 | 2008-12-19 | 울산대학교 산학협력단 | The method to give peak codes using of region partition of frequency signals, the peak codes thereof and the method to predict fault of the machines using the peak codes |
US8094034B2 (en) | 2007-09-18 | 2012-01-10 | Georgia Tech Research Corporation | Detecting actuation of electrical devices using electrical noise over a power line |
US7938935B2 (en) | 2007-10-11 | 2011-05-10 | Honeywell Asca Inc. | Infrared measurement of paper machine clothing condition |
US8112381B2 (en) | 2007-11-02 | 2012-02-07 | Siemens Corporation | Multivariate analysis of wireless sensor network data for machine condition monitoring |
US8571904B2 (en) | 2008-02-08 | 2013-10-29 | Rockwell Automation Technologies, Inc. | Self sensing component interface system |
US8355710B2 (en) | 2008-05-09 | 2013-01-15 | Research In Motion Limited | System and method of initiating user notification for a wireless device |
WO2010011918A2 (en) | 2008-07-24 | 2010-01-28 | University Of Cincinnati | Methods for prognosing mechanical systems |
US10101219B2 (en) | 2008-09-05 | 2018-10-16 | The Research Foundation For The State University Of New York | Carbon nanotube sensing system, carbon nanotube dew point hygrometer, method of use thereof and method of forming a carbon nanotube dew point hygrometer |
US8196207B2 (en) | 2008-10-29 | 2012-06-05 | Bank Of America Corporation | Control automation tool |
EP2186613B1 (en) | 2008-11-17 | 2013-05-29 | Piovan S.P.A. | High-efficiency system for dehumidifying and/or drying plastic materials |
FR2939924B1 (en) | 2008-12-15 | 2012-10-12 | Snecma | IDENTIFICATION OF FAILURES IN AN AIRCRAFT ENGINE |
US8726535B2 (en) | 2008-12-16 | 2014-05-20 | Pioneer Hi Bred International Inc | Method, apparatus and system for controlling heated air drying |
US8149128B2 (en) | 2009-03-10 | 2012-04-03 | The United States Of America As Represented By The United States Department Of Energy | Ground potential rise monitor |
FR2947309A1 (en) | 2009-06-26 | 2010-12-31 | Alcatel Lucent | METHOD FOR PREDICTING A ROTOR ROTATION FAILURE OF A VACUUM PUMP AND ASSOCIATED PUMPING DEVICE |
US20110016199A1 (en) | 2009-07-17 | 2011-01-20 | Phil De Carlo | System for electronic device monitoring |
JP5363927B2 (en) | 2009-09-07 | 2013-12-11 | 株式会社日立製作所 | Abnormality detection / diagnosis method, abnormality detection / diagnosis system, and abnormality detection / diagnosis program |
US8405940B2 (en) | 2009-10-13 | 2013-03-26 | Schweitzer Engineering Laboratories Inc | Systems and methods for generator ground fault protection |
US8660875B2 (en) | 2009-11-02 | 2014-02-25 | Applied Materials, Inc. | Automated corrective and predictive maintenance system |
US8390299B2 (en) | 2009-11-24 | 2013-03-05 | Fluke Corporation | Earth ground tester with remote control |
US8442688B2 (en) | 2010-01-28 | 2013-05-14 | Holcim (US), Inc. | System for monitoring plant equipment |
CN201672991U (en) | 2010-02-24 | 2010-12-15 | 管于球 | Dry and wet bulb temperature acquisition device |
US20110216805A1 (en) | 2010-03-02 | 2011-09-08 | Fernando C J Anthony | Dissolution testing with infrared temperature measurement |
US8862428B2 (en) | 2010-04-01 | 2014-10-14 | Thomas Martin Lill | Machine or device monitoring and alert method and system |
EP2386675B1 (en) | 2010-05-14 | 2014-07-16 | Electrolux Home Products Corporation N.V. | Heating circuit with monitoring arrangement for a household appliance |
US8920078B2 (en) | 2010-06-03 | 2014-12-30 | Jason Woolever | Blower controller for pneumatic conveyance of granular materials |
US20120045068A1 (en) | 2010-08-20 | 2012-02-23 | Korea Institute Of Science And Technology | Self-fault detection system and method for microphone array and audio-based device |
US20120213098A1 (en) | 2011-02-21 | 2012-08-23 | Future Wireless Tech LLC | Real-time and synchronization Internet of things analyzer System Architecture |
EP2700061A4 (en) | 2011-04-22 | 2014-11-19 | Expanergy Llc | Systems and methods for analyzing energy usage |
US8972067B2 (en) | 2011-05-11 | 2015-03-03 | General Electric Company | System and method for optimizing plant operations |
WO2012172971A1 (en) | 2011-06-13 | 2012-12-20 | 新日鉄住金化学株式会社 | Sensor element, dew condensation sensor, humidity sensor, method for detecting dew condensation, and dew-point measurement device |
US20120330614A1 (en) | 2011-06-22 | 2012-12-27 | Honeywell International Inc. | Rule-based diagnostics apparatus and method for rotating machinery |
US20120330499A1 (en) | 2011-06-23 | 2012-12-27 | United Technologies Corporation | Acoustic diagnostic of fielded turbine engines |
US20130170417A1 (en) | 2011-09-06 | 2013-07-04 | Evan A. Thomas | Distributed low-power monitoring system |
CN103019158A (en) | 2011-09-20 | 2013-04-03 | 朗德华信(北京)自控技术有限公司 | System and method for managing and controlling green building facilities and equipment based on cloud computing |
WO2013041440A1 (en) | 2011-09-20 | 2013-03-28 | Abb Technology Ag | System and method for plant wide asset management |
US8560368B1 (en) | 2011-11-18 | 2013-10-15 | Lockheed Martin Corporation | Automated constraint-based scheduling using condition-based maintenance |
EP2795315A2 (en) | 2011-12-20 | 2014-10-29 | Bry-Air (Asia) Pvt. Ltd. | Method and device for moisture determination and control |
US9157829B2 (en) | 2011-12-30 | 2015-10-13 | Spirax-Sarco Limited | Apparatus and method for monitoring a steam plant |
CN102539911A (en) | 2012-01-13 | 2012-07-04 | 广东电网公司电力科学研究院 | Intelligent electric energy metering system under internet-of-things architecture |
US9612051B2 (en) | 2012-02-09 | 2017-04-04 | Vincent G. LoTempio | Heat reactivated process for desiccant air dryer systems using blower purge and method therefore |
US20140310379A1 (en) | 2013-04-15 | 2014-10-16 | Flextronics Ap, Llc | Vehicle initiated communications with third parties via virtual personality |
KR101914079B1 (en) | 2012-04-04 | 2019-01-14 | 삼성전자주식회사 | Method for diagnosing error of home appliance device of error diagnositc system and apparatus therefor |
US9275334B2 (en) | 2012-04-06 | 2016-03-01 | Applied Materials, Inc. | Increasing signal to noise ratio for creation of generalized and robust prediction models |
US8931952B2 (en) | 2012-04-27 | 2015-01-13 | Par Technology Corporation | Temperature monitoring device for workflow monitoring system |
US9367803B2 (en) | 2012-05-09 | 2016-06-14 | Tata Consultancy Services Limited | Predictive analytics for information technology systems |
US20130304677A1 (en) | 2012-05-14 | 2013-11-14 | Qualcomm Incorporated | Architecture for Client-Cloud Behavior Analyzer |
US10520397B2 (en) | 2012-05-31 | 2019-12-31 | University Of Connecticut | Methods and apparatuses for defect diagnosis in a mechanical system |
FI128899B (en) | 2012-09-19 | 2021-02-26 | Konecranes Oyj | Predictive maintenance method and system |
US10365332B2 (en) | 2012-11-02 | 2019-07-30 | Analog Devices Global Unlimited Company | System and method to reduce data handling on lithium ion battery monitors |
US8915863B2 (en) | 2012-11-16 | 2014-12-23 | L. Zane Shuck | In vivo device and method for researching GI tract processes, microbes, and variables associated with illnesses and diseases |
US9223299B2 (en) | 2012-11-30 | 2015-12-29 | Discovery Sound Technology, Llc | Equipment sound monitoring system and method |
US20150311721A1 (en) | 2012-12-07 | 2015-10-29 | Nuevo Power, Inc. | Remote access, control, and management of a power micro grid |
US9062536B2 (en) | 2013-01-02 | 2015-06-23 | Graco Minnesota Inc. | Devices and methods for landfill gas well monitoring and control |
US20140207394A1 (en) | 2013-01-23 | 2014-07-24 | JB Industries, Inc. | Systems and methods for communicating with heating, ventilation and air conditioning equipment |
CA2816469C (en) | 2013-01-31 | 2020-01-21 | Spielo International Canada Ulc | Faults and performance issue prediction |
US9552535B2 (en) | 2013-02-11 | 2017-01-24 | Emotient, Inc. | Data acquisition for machine perception systems |
US10257665B2 (en) | 2013-02-25 | 2019-04-09 | Qualcomm Incorporated | Analytics engines for IoT devices |
US9804588B2 (en) | 2014-03-14 | 2017-10-31 | Fisher-Rosemount Systems, Inc. | Determining associations and alignments of process elements and measurements in a process |
CA2906287C (en) | 2013-03-15 | 2018-05-08 | Stride Tool, Inc. | Smart hvac manifold system |
US9786197B2 (en) | 2013-05-09 | 2017-10-10 | Rockwell Automation Technologies, Inc. | Using cloud-based data to facilitate enhancing performance in connection with an industrial automation system |
US9438648B2 (en) | 2013-05-09 | 2016-09-06 | Rockwell Automation Technologies, Inc. | Industrial data analytics in a cloud platform |
US20140336791A1 (en) | 2013-05-09 | 2014-11-13 | Rockwell Automation Technologies, Inc. | Predictive maintenance for industrial products using big data |
CN203362223U (en) | 2013-06-05 | 2013-12-25 | 桂林电子科技大学 | Mine automatic drainage system based on Internet of things |
CN103399486B (en) | 2013-07-05 | 2016-04-06 | 杭州电子科技大学 | Plastic drying machine temperature optimization energy-saving control method |
US20150026044A1 (en) | 2013-07-07 | 2015-01-22 | Gaonic Ltd. | Method, protocol and system for universal sensor communication |
US9244042B2 (en) | 2013-07-31 | 2016-01-26 | General Electric Company | Vibration condition monitoring system and methods |
CN104376849A (en) | 2013-08-14 | 2015-02-25 | Abb技术有限公司 | System and method for distinguishing sounds, state monitoring system and mobile telephone |
CN203588054U (en) | 2013-11-09 | 2014-05-07 | 山西同昌信息技术实业有限公司 | Power environment sensor monitoring system based on technology of Internet of things |
CN105899960B (en) | 2013-11-19 | 2020-12-04 | 李铉昌 | Mobile leakage detection device and method |
US20150181313A1 (en) | 2013-12-20 | 2015-06-25 | Aktiebolaget Skf | Method of operating an infrared temperature magnet with an rfid antenna |
US9766270B2 (en) | 2013-12-30 | 2017-09-19 | Fluke Corporation | Wireless test measurement |
US9494364B2 (en) | 2014-02-28 | 2016-11-15 | Process Control Corporation | Dryer hopper |
CN104036614A (en) | 2014-06-30 | 2014-09-10 | 广西大学 | Goods transportation fire warning system based on technology of internet of things |
US20160291552A1 (en) | 2014-11-18 | 2016-10-06 | Prophecy Sensors, Llc | System for rule management, predictive maintenance and quality assurance of a process and machine using reconfigurable sensor networks and big data machine learning |
US9826338B2 (en) | 2014-11-18 | 2017-11-21 | Prophecy Sensorlytics Llc | IoT-enabled process control and predective maintenance using machine wearables |
US9785126B2 (en) | 2014-11-25 | 2017-10-10 | Rockwell Automation Technologies, Inc. | Inferred energy usage and multiple levels of energy usage |
US9563986B2 (en) | 2014-12-31 | 2017-02-07 | Ebay Inc. | Systems and methods for multi-signal fault analysis |
US10599982B2 (en) | 2015-02-23 | 2020-03-24 | Machinesense, Llc | Internet of things based determination of machine reliability and automated maintainenace, repair and operation (MRO) logs |
US20160245279A1 (en) | 2015-02-23 | 2016-08-25 | Biplab Pal | Real time machine learning based predictive and preventive maintenance of vacuum pump |
US20160313216A1 (en) | 2015-04-25 | 2016-10-27 | Prophecy Sensors, Llc | Fuel gauge visualization of iot based predictive maintenance system using multi-classification based machine learning |
US10613046B2 (en) | 2015-02-23 | 2020-04-07 | Machinesense, Llc | Method for accurately measuring real-time dew-point value and total moisture content of a material |
US20160245686A1 (en) | 2015-02-23 | 2016-08-25 | Biplab Pal | Fault detection in rotor driven equipment using rotational invariant transform of sub-sampled 3-axis vibrational data |
KR101628276B1 (en) | 2015-04-20 | 2016-06-08 | 주식회사 루닛 | System and method for pathological analysis based on cloud |
US9823289B2 (en) | 2015-06-01 | 2017-11-21 | Prophecy Sensorlytics Llc | Automated digital earth fault system |
CA3128629A1 (en) | 2015-06-05 | 2016-07-28 | C3.Ai, Inc. | Systems and methods for data processing and enterprise ai applications |
EP3329433A1 (en) | 2015-07-29 | 2018-06-06 | Illinois Tool Works Inc. | System and method to facilitate welding software as a service |
US10007513B2 (en) | 2015-08-27 | 2018-06-26 | FogHorn Systems, Inc. | Edge intelligence platform, and internet of things sensor streams system |
US10178177B2 (en) | 2015-12-08 | 2019-01-08 | Honeywell International Inc. | Apparatus and method for using an internet of things edge secure gateway |
US9866637B2 (en) | 2016-01-11 | 2018-01-09 | Equinix, Inc. | Distributed edge processing of internet of things device data in co-location facilities |
US10275573B2 (en) | 2016-01-13 | 2019-04-30 | Bigfoot Biomedical, Inc. | User interface for diabetes management system |
US9781243B1 (en) | 2016-06-27 | 2017-10-03 | Intel Corporation | Optimizing wearable device settings using machine learning |
US10041844B1 (en) | 2017-04-07 | 2018-08-07 | International Business Machines Corporation | Fluid flow rate assessment by a non-intrusive sensor in a fluid transfer pump system |
-
2015
- 2015-02-23 US US14/628,322 patent/US20160245279A1/en not_active Abandoned
-
2019
- 2019-01-22 US US16/253,462 patent/US11002269B2/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080109185A1 (en) * | 2004-12-17 | 2008-05-08 | Korea Research Institute Of Standsards And Science | Precision Diagnostic Method For The Failure Protection And Predictive Maintenance Of A Vacuum Pump And A Precision Diagnostic System Therefor |
US20130201316A1 (en) * | 2012-01-09 | 2013-08-08 | May Patents Ltd. | System and method for server based control |
US20150139817A1 (en) * | 2013-11-19 | 2015-05-21 | Gardner Denver Thomas, Inc. | Ramp-up optimizing vacuum system |
Cited By (50)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9826338B2 (en) | 2014-11-18 | 2017-11-21 | Prophecy Sensorlytics Llc | IoT-enabled process control and predective maintenance using machine wearables |
US10638295B2 (en) | 2015-01-17 | 2020-04-28 | Machinesense, Llc | System and method for turbomachinery preventive maintenance and root cause failure determination |
US10959077B2 (en) | 2015-01-17 | 2021-03-23 | Machinesense Llc | Preventive maintenance and failure cause determinations in turbomachinery |
US10598520B2 (en) | 2015-02-23 | 2020-03-24 | Machinesense, Llc | Method and apparatus for pneumatically conveying particulate material including a user-visible IoT-based classification and predictive maintenance system noting maintenance state as being acceptable, cautionary, or dangerous |
US11092466B2 (en) | 2015-02-23 | 2021-08-17 | Machinesense, Llc | Internet of things based conveyance having predictive maintenance |
US11162837B2 (en) | 2015-02-23 | 2021-11-02 | Machinesense, Llc | Detecting faults in rotor driven equipment |
US11002269B2 (en) | 2015-02-23 | 2021-05-11 | Machinesense, Llc | Real time machine learning based predictive and preventive maintenance of vacuum pump |
US10969356B2 (en) | 2015-02-23 | 2021-04-06 | Machinesense, Llc | Methods for measuring real-time dew-point value and total moisture content of material to be molded or extruded |
US10613046B2 (en) | 2015-02-23 | 2020-04-07 | Machinesense, Llc | Method for accurately measuring real-time dew-point value and total moisture content of a material |
US10599982B2 (en) | 2015-02-23 | 2020-03-24 | Machinesense, Llc | Internet of things based determination of machine reliability and automated maintainenace, repair and operation (MRO) logs |
US10271115B2 (en) * | 2015-04-08 | 2019-04-23 | Itt Manufacturing Enterprises Llc. | Nodal dynamic data acquisition and dissemination |
US10745263B2 (en) * | 2015-05-28 | 2020-08-18 | Sonicu, Llc | Container fill level indication system using a machine learning algorithm |
US9823289B2 (en) | 2015-06-01 | 2017-11-21 | Prophecy Sensorlytics Llc | Automated digital earth fault system |
US11300358B2 (en) | 2015-08-23 | 2022-04-12 | Prophecy Sensorlytics, Llc | Granular material dryer for process of resin material prior to molding or extrusion |
US11268760B2 (en) | 2015-08-23 | 2022-03-08 | Prophecy Sensorlytics, Llc | Dryer machine learning predictive maintenance method and apparatus |
US10648735B2 (en) | 2015-08-23 | 2020-05-12 | Machinesense, Llc | Machine learning based predictive maintenance of a dryer |
US10481195B2 (en) | 2015-12-02 | 2019-11-19 | Machinesense, Llc | Distributed IoT based sensor analytics for power line diagnosis |
US11486401B2 (en) | 2015-12-17 | 2022-11-01 | Wayne/Scott Fetzer Company | Integrated sump pump controller with status notifications |
US10711788B2 (en) | 2015-12-17 | 2020-07-14 | Wayne/Scott Fetzer Company | Integrated sump pump controller with status notifications |
US10913549B1 (en) | 2016-03-28 | 2021-02-09 | Amazon Technologies, Inc. | Automated aerial vehicle inspections |
US10650621B1 (en) | 2016-09-13 | 2020-05-12 | Iocurrents, Inc. | Interfacing with a vehicular controller area network |
US11232655B2 (en) | 2016-09-13 | 2022-01-25 | Iocurrents, Inc. | System and method for interfacing with a vehicular controller area network |
CN106640690A (en) * | 2016-12-28 | 2017-05-10 | 上海市城市排水有限公司机修安装分公司 | Real-time long-range monitoring system for drainage pump and real-time long-range monitoring method of real-time long-range monitoring system |
CN107013449A (en) * | 2017-04-18 | 2017-08-04 | 山东万腾电子科技有限公司 | Voice signal based on deep learning recognizes the method and system of compressor fault |
USD893552S1 (en) | 2017-06-21 | 2020-08-18 | Wayne/Scott Fetzer Company | Pump components |
USD1015378S1 (en) | 2017-06-21 | 2024-02-20 | Wayne/Scott Fetzer Company | Pump components |
US11263876B2 (en) * | 2017-09-28 | 2022-03-01 | Ncr Corporation | Self-service terminal (SST) maintenance and support processing |
US10810501B1 (en) * | 2017-10-20 | 2020-10-20 | Amazon Technologies, Inc. | Automated pre-flight and in-flight testing of aerial vehicles by machine learning |
US10921792B2 (en) | 2017-12-21 | 2021-02-16 | Machinesense Llc | Edge cloud-based resin material drying system and method |
US10839506B1 (en) | 2018-01-02 | 2020-11-17 | Amazon Technologies, Inc. | Detecting surface flaws using computer vision |
USD890211S1 (en) | 2018-01-11 | 2020-07-14 | Wayne/Scott Fetzer Company | Pump components |
USD1014560S1 (en) | 2018-01-11 | 2024-02-13 | Wayne/Scott Fetzer Company | Pump components |
US10921777B2 (en) | 2018-02-15 | 2021-02-16 | Online Development, Inc. | Automated machine analysis |
WO2019182894A1 (en) * | 2018-03-19 | 2019-09-26 | Ge Inspection Technologies, Lp | Diagnosing and predicting electrical pump operation |
CN108757502A (en) * | 2018-05-15 | 2018-11-06 | 江苏大学 | A kind of water pump assembly typical case's health status monitoring device and method based on Internet of Things |
WO2019218408A1 (en) * | 2018-05-15 | 2019-11-21 | 江苏大学 | Internet of things-based device and method for monitoring typical health status of pump unit |
CN110553093A (en) * | 2018-06-01 | 2019-12-10 | 西门子股份公司 | Method for monitoring a mechanical system |
EP3575909A1 (en) * | 2018-06-01 | 2019-12-04 | Siemens Aktiengesellschaft | Method for monitoring a mechanical system |
US11359744B2 (en) | 2018-06-01 | 2022-06-14 | Siemens Aktiengesellschaft | Method for monitoring a mechanical system |
US10789785B2 (en) * | 2018-06-11 | 2020-09-29 | Honeywell International Inc. | Systems and methods for data collection from maintenance-prone vehicle components |
US11495061B2 (en) | 2018-06-11 | 2022-11-08 | Honeywell International Inc. | Systems and methods for data collection from maintenance-prone vehicle components |
US11097856B1 (en) | 2019-02-18 | 2021-08-24 | Amazon Technologies, Inc. | Determining integrity of acoustically excited objects |
CN109882401A (en) * | 2019-04-15 | 2019-06-14 | 湖南中普新能源科技有限公司 | Air compressor machine self-test strategy based on Internet of Things |
CN110705133A (en) * | 2019-11-06 | 2020-01-17 | 中国联合网络通信集团有限公司 | Predictive maintenance method and predictive maintenance equipment |
CN111706499A (en) * | 2020-06-09 | 2020-09-25 | 成都数之联科技有限公司 | Predictive maintenance system and method for vacuum pump and automatic vacuum pump purchasing system |
CN112628132A (en) * | 2020-12-24 | 2021-04-09 | 上海大学 | Water pump key index prediction method based on machine learning |
CN112628132B (en) * | 2020-12-24 | 2022-04-26 | 上海大学 | Water pump key index prediction method based on machine learning |
CN113127473A (en) * | 2021-05-18 | 2021-07-16 | 浙江太美医疗科技股份有限公司 | Method, system and computer readable medium for processing medical data |
CN113688011A (en) * | 2021-08-26 | 2021-11-23 | 广东鑫钻节能科技股份有限公司 | Screw blower gas station control system based on Internet of things |
CN115076089A (en) * | 2022-07-06 | 2022-09-20 | 中南大学 | Hydraulic pump flow distribution pair oil film characteristic online test device based on end cover transformation |
Also Published As
Publication number | Publication date |
---|---|
US11002269B2 (en) | 2021-05-11 |
US20190154032A1 (en) | 2019-05-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11002269B2 (en) | Real time machine learning based predictive and preventive maintenance of vacuum pump | |
US10598520B2 (en) | Method and apparatus for pneumatically conveying particulate material including a user-visible IoT-based classification and predictive maintenance system noting maintenance state as being acceptable, cautionary, or dangerous | |
US20170178311A1 (en) | Machine fault detection based on a combination of sound capture and on spot feedback | |
US10425449B2 (en) | Classifying internet-of-things (IOT) gateways using principal component analysis | |
US10855800B2 (en) | Managing device profiles in the Internet-of-Things (IoT) | |
US10599982B2 (en) | Internet of things based determination of machine reliability and automated maintainenace, repair and operation (MRO) logs | |
US20180234326A1 (en) | Device identity augmentation | |
US10539932B2 (en) | Machine diagnostics based on overall system energy state | |
US11268760B2 (en) | Dryer machine learning predictive maintenance method and apparatus | |
US11209807B2 (en) | Anomaly detection | |
US11494252B2 (en) | System and method for detecting anomalies in cyber-physical system with determined characteristics | |
EP3311310A1 (en) | Cross-domain time series data conversion apparatus, methods, and systems | |
CN107111590A (en) | Monitoring and control system using cloud service | |
WO2017201345A1 (en) | Systems and methods for equipment performance modeling | |
US11556820B2 (en) | Method and system for a dynamic data collection and context-driven actions | |
JP4635194B2 (en) | Anomaly detection device | |
US20200284938A1 (en) | Remote vibration detection of submerged equipment using magnetic field sensing | |
US11348013B2 (en) | Determining, encoding, and transmission of classification variables at end-device for remote monitoring | |
CN112005181B (en) | Abnormality detection | |
EP4354244A1 (en) | Anomaly detection for industrial assets | |
EP4057093A1 (en) | Condition monitoring of rotating machines |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: PROPHECY SENSORLYTICS LLC, MARYLAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GILLMEISTER, STEVEN;REEL/FRAME:038956/0058 Effective date: 20151214 |
|
AS | Assignment |
Owner name: PROPHECY SENSORLYTICS LLC, MARYLAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:PUROHIT, AMIT;REEL/FRAME:038984/0896 Effective date: 20151211 |
|
AS | Assignment |
Owner name: PROPHECY SENSORS, LLC, MARYLAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:PAL, BIPLAB, PHD;REEL/FRAME:038995/0895 Effective date: 20160622 |
|
AS | Assignment |
Owner name: PROPHECY SENSORS, LLC, MARYLAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:PROPHECY SENSORLYTICS LLC;REEL/FRAME:039028/0195 Effective date: 20160627 |
|
AS | Assignment |
Owner name: PROPHECY SENSORLYTICS, LLC, MARYLAND Free format text: CHANGE OF NAME;ASSIGNOR:PROPHECY SENSORS, LLC;REEL/FRAME:043891/0462 Effective date: 20160909 |
|
AS | Assignment |
Owner name: MACHINESENSE, LLC, MARYLAND Free format text: CHANGE OF NAME;ASSIGNOR:PROPHECY SENSORLYTICS, LLC;REEL/FRAME:044366/0373 Effective date: 20171027 |
|
STCV | Information on status: appeal procedure |
Free format text: NOTICE OF APPEAL FILED |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |