WO2022195594A1 - Condition-based monitoring module, a system and a method of using the same - Google Patents

Condition-based monitoring module, a system and a method of using the same Download PDF

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Publication number
WO2022195594A1
WO2022195594A1 PCT/IL2022/050301 IL2022050301W WO2022195594A1 WO 2022195594 A1 WO2022195594 A1 WO 2022195594A1 IL 2022050301 W IL2022050301 W IL 2022050301W WO 2022195594 A1 WO2022195594 A1 WO 2022195594A1
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WO
WIPO (PCT)
Prior art keywords
module
cbm
condition
based monitoring
machine
Prior art date
Application number
PCT/IL2022/050301
Other languages
French (fr)
Inventor
Boaz Almog
Guy ZOHAR
Original Assignee
Boaz Almog
Zohar Guy
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Boaz Almog, Zohar Guy filed Critical Boaz Almog
Publication of WO2022195594A1 publication Critical patent/WO2022195594A1/en

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors
    • H02P29/02—Providing protection against overload without automatic interruption of supply
    • H02P29/024—Detecting a fault condition, e.g. short circuit, locked rotor, open circuit or loss of load
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0428—Safety, monitoring

Definitions

  • the present invention relates to machine maintenance. More specifically, the present invention relates to monitoring the operation of a machine, detecting and diagnosing of machine abnormalities in real time.
  • Condition-based monitoring enables early detection and diagnosis of asset anomalies in real time.
  • the circuit includes a power source and a communication module to transmit the data outside (usually to the cloud) for further processing.
  • the circuit is always encased inside a tough dedicated box with means of attaching it to existing gear (screws, clamps, etc.).
  • An improved condition-based monitoring product that is a standalone product characterized by a compact footprint.
  • An improved condition-based monitoring product that is extremely sensitive to physical and electrical/magnetic properties of the machine.
  • condition-based monitoring (CBM) module for monitoring machine operation and for detecting anomalies in said machine in real time.
  • the condition-based monitoring (CBM) module comprising at least:
  • condition-based monitoring module is a complete functional unit mounted on a host circuit board of the machine, therefore, receives power from said host circuit board of the machine and optionally receives information/data directly from electric components on the machine electric circuits, said condition-based monitoring module operates independently of the host circuit, said condition-based monitoring module monitors the operation of said machine for detecting and analyzing machine anomalies in real time.
  • condition-based monitoring (CBM) module further comprising a processing program performing calculations and data analysis and generating machine operational parameters.
  • the processing program is comprised of artificial intelligence (AI) models.
  • AI artificial intelligence
  • the machine operational parameters including rotating speed estimates, statistical profiles, physical profiles, and diagnostic profiles.
  • algorithms of said processing program and/or parameters of said algorithms are adjusted and/or optimized by receiving upgrades from an external server.
  • condition-based monitoring (CBM) module collecting external and/or internal sensory data and processing the data by said processing program for detecting anomalies.
  • the at least one onboard sensor detects parameters selected from rotation speed, acceleration, production yield, temperature, magnetic field, acoustic, moisture, air pressure, and mechanical stress.
  • condition-based monitoring (CBM) module transmitting said external and/or internal sensory data and/or said processing the data to said server for processing, thus, for detecting anomalies.
  • condition-based monitoring (CBM) module does not alter the machine performance.
  • condition-based monitoring (CBM) module is packaged by “through-hole” or “surface mount” type packaging.
  • the packaging is either system in package (SIP) or dual in line package (DIP).
  • the condition-based monitoring (CBM) module operates at voltages between 3-5V.
  • the condition-based monitoring (CBM) support secure communication (SSL/TLS).
  • the condition-based monitoring (CBM) module support standard industrial communication protocols (TCP/IP, MQTT).
  • TCP/IP standard industrial communication protocols
  • MQTT standard industrial communication protocols
  • the condition-based monitoring (CBM) module has input and output voltage lines to/from the host circuit board, said input and output voltage lines are selected from:
  • each condition-based monitoring (CBM) module has an ID for identifying said condition- based monitoring (CBM) module and the machine within which it is installed.
  • a system for monitoring machine operation and for detecting anomalies in said machine in real time comprises: the above-described condition-based monitoring (CBM) module and a local/cloud server configured to monitor the machine operation for detecting and diagnosing machine anomalies associated with machine components in real time, wherein the condition-based monitoring (CBM) module transfers sensory data and/or processed data to the server for machine anomaly detection and analysis.
  • the server generates machine analytics using features produced and provided by the condition- based monitoring (CBM) module.
  • CBM condition- based monitoring
  • the server detects machine anomalies by comparing statistical distribution of the extracted profiles to saved anomaly type profiles.
  • the server transfers updated parameters to algorithms running on the PCB module 100.
  • a method for monitoring machine operation and for detecting anomalies in said machine in real time comprises: mounting the above-mentioned condition-based monitoring (CBM) module on a host circuit board of a machine; receiving power from said host circuit board of the machine; optionally receiving information/data directly from electric components on the machine electric circuits, wherein said condition-based monitoring module operating independently of the host circuit, wherein said condition-based monitoring module monitors the operation of said machine for detecting and analyzing machine anomalies in real time.
  • CBM condition-based monitoring
  • the method further comprising connecting and configuring power input lines and output lines of the condition-based monitoring (CBM).
  • CBM condition-based monitoring
  • the method further comprising adding wiring for monitoring voltages/currents coming from the host circuit.
  • the method further comprising downloading and installing updated firmware on the condition-based monitoring (CBM) module.
  • CBM condition-based monitoring
  • downloading and installing updated firmware on the condition-based monitoring (CBM) module comprising:
  • Fig. 1 illustrates a condition-based monitoring (CBM) module mountable on a hosting board in accordance with some embodiments of the present invention.
  • CBM condition-based monitoring
  • Figs. 2A&B illustrate the condition-based monitoring (CBM) module of the present invention mounted on a hosting main circuit board of a host machine in accordance with some embodiments of the present invention.
  • CBM condition-based monitoring
  • Fig. 3 illustrates a monitoring system in accordance with some embodiments of the present invention.
  • Fig. 4 illustrates a method for setting the CBM module in accordance with some embodiments of the present invention.
  • Fig. 5 is a flowchart of a procedure used for setting updates of the CBM module in accordance with some embodiments of the present invention.
  • the present invention provides a preferred solution for machine manufacturers who wish to include condition-based monitoring (CBM) capabilities in their products.
  • CBM condition-based monitoring
  • the present invention is of a condition-based monitoring (CBM) module mountable on a host printed circuit.
  • CBM condition-based monitoring
  • machine manufacturers who wish to upgrade their product i.e., their host machines which may be of various types and models, have to:
  • Fig. 1 illustrates a condition-based monitoring (CBM) module 100 mountable on a hosting board in accordance with some embodiments of the present invention.
  • CBM condition-based monitoring
  • the CBM module 100 is a complete functional unit that includes at least the following modules:
  • condition-based monitoring (CBM) 100 is a complete functional unit comprised of components for collecting external and/or internal data, for processing the data by algorithm(s), for instance, by artificial intelligence (AI) models onboard, and for transmitting data, raw sensory data and/or processed data, to a cloud server (or a local server) for further processing, i.e., for detection and diagnosis of machine anomalies in real time.
  • algorithm(s) for instance, by artificial intelligence (AI) models onboard
  • AI artificial intelligence
  • the algorithms of the condition-based monitoring (CBM) 100 are being updated continuously via an external server (local server/cloud server).
  • Such algorithms of the condition-based monitoring (CBM) 100 are continuously updated to improve and increase the capabilities of the condition-based monitoring (CBM) 100.
  • the advantage of the CBM module 100 is four-fold:
  • the CBM module 100 has no impact on the original design and on the performance of the PCB (host product).
  • the CBM module 100 operates independently of the host circuit and optionally may not get any inputs/output from/to the host circuit board, thus, cannot affect the performance of the host circuit board - any breach to the CBM module (which has remote communication capabilities) cannot harm the host and its performance;
  • the main board is already encased in a physically protective box, and since the CBM module 100 is mounted on the main board, no additional space/casings and power wirings are required, hence, saving on substantial space and having minimal effect on the product design; and (d) The CBM module 100 can have access to calculated data such as, for instance, machine related performance, operation schedule and the like via host sensors on the main board.
  • the CBM module 100 may be packaged using one of many possible integrated circuit packaging types such as system in package (SIP), dual in line package (DIP) and more.
  • SIP system in package
  • DIP dual in line package
  • the packaging can be of “Through-hole” or “surface mount” types.
  • the CBM module 100 may operate at various temperatures and preferably at industrial grade operating temperatures ranging from -40°C to 85°C.
  • the CBM module 100 may operate at various voltages and preferably at voltages between 3-5V.
  • the CBM module 100 may have a power consumption of less than 1 W.
  • the CBM module 100 may support secure communication (SSL/TLS).
  • SSL/TLS secure communication
  • the CBM module 100 may support standard industrial communication protocols (TCP/IP, MQTT).
  • Figs. 2A&B illustrate the condition-based monitoring (CBM) module 100 of the present invention mounted on a hosting main circuit board 200 of a host machine in accordance with some embodiments of the present invention.
  • CBM condition-based monitoring
  • the CBM module 100 is a complete functional circuit board which operates independently of the host machine control circuit.
  • the CBM module 100 is mounted on the main (mother) circuit board 200 of the machine and receives power from the main circuit board 200, for instance, via contact pads.
  • the CBM module 100 may include, or may be coupled to, one or more sensors configured to sense information of the machine. Such information/data may indicate the load and functionality of the electronic components, thus, entails the CBM module 100 to detect and diagnose machine anomalies associated with various machine components, such as a motor, a pump, a conveyor belt and the like.
  • the CBM module 100 ‘feels’ the physical and electrical/magnetic properties of the machine. This allows, for example, to test machine parameters such as vibrations, position, speed, acceleration, energy profiles, magnetic field, temperature, or acoustic information produced by the rotational movement of machine components.
  • Step motor drivers for instance, consume a lot of power, and hence, release a great amount of heat and emit magnetic fields. Monitoring the heat release and the magnetic field continuously allows effective monitoring of the step motor driver.
  • the at least one onboard sensor detects process related parameters such as rotation speed of a motor, acceleration, production yield, temperature at a certain point, magnetic field, acoustic, moisture, air pressure, mechanical stress and the like.
  • the CBM module 100 may be connected to external sensors such as temperature sensors and the like.
  • the CBM module 100 receives information/data directly from electric components on the machine electric circuits.
  • the CBM module 100 may get essential information/data points via monitoring electrical elements such as voltage, current, impedance; stress, strain, and the like on the circuit.
  • Such data points are highly essential for detecting and diagnosing machine anomalies.
  • the CBM module 100 may process the information/data and generate machine operational parameters such as rotating speed estimate, statistical or physical profiles, and diagnostic profiles indicative of various types of faults.
  • the CBM module 100 may be programmed to monitor the machine continuously when the machine operates in its normal operating conditions.
  • the CBM module 100 may perform calculations and data analysis onboard before transmitting the data to the cloud server 302.
  • the CBM module 100 may include machine learning algorithms to identify anomalies in the machine.
  • the machine learning (ML) algorithms may include unsupervised learning to identify multiple machine working states. Such algorithms may include supervised algorithms such as random forest.
  • the ML algorithms may classify the anomalies according to predefined parameters.
  • the parameters of the algorithms can be updated via a centralized database and may be customized according to the machine type and/or specific demands.
  • the CBM module 100 has inputs and output lines from/to the hosting main circuit board 200.
  • the CBM module 100 is connected to output lines of the hosting main circuit board 200.
  • Examples for such output lines may include:
  • CBM status indication for instance, CBM module network online/offline, error with CBM module and the like.
  • sensor 1 has input and output lines from/to the hosting main circuit board 200 for monitoring the machine speed
  • sensor 2 has inputs and output lines from/to the hosting main circuit board 200 for detecting the operating mode
  • sensor 3 has input and output lines from/to the hosting main circuit board 200 for monitoring the fault indicator output
  • sensor 5 has inputs and output lines from/to the hosting main circuit board 200 for monitoring the operating control.
  • Fig. 3 illustrates a monitoring system 300 in accordance with some embodiments of the present invention.
  • the monitoring system 300 comprises the CBM module 100 and a cloud server 302 (or a local server) configured to provide a cloud-based service that can be configured to detect and isolate machine anomalies associated with various machine components, such motors, gears, bearings, transmission, or other components.
  • a cloud server 302 or a local server configured to provide a cloud-based service that can be configured to detect and isolate machine anomalies associated with various machine components, such motors, gears, bearings, transmission, or other components.
  • the sensory data received via the CBM module 100, is transferred to the accompanied processing program in CPU 102 which communicates with a processing program 304 in a cloud server 302 via data communication means, communication module 108, such as, for instance, Smart Mesh IP protocol, Narrow band IOT (cellular network), ethemet (local network) and Wi-fi.
  • communication module 108 such as, for instance, Smart Mesh IP protocol, Narrow band IOT (cellular network), ethemet (local network) and Wi-fi.
  • the cloud server 302 may be a computing device configured to provide cloud-based service (storage, analytics, maintenance and the like).
  • a machine analytics may be generated at the cloud 302 using the features produced and provided by the CBM module 100.
  • the machine anomaly may be detected using statistical distributions of a temporal or spectral profile and the like. Anomaly detection may involve comparing the extracted profile, or a statistical distribution of the extracted profile, to saved data containing various anomaly type profiles.
  • the cloud server 302 may transfer updated parameters to the algorithms running on the PCB module 100.
  • the user may be able to see the detected anomaly(s) via mobile devices in communication with the cloud such as mobile phones, PCs, and the like.
  • the user may view the status, interpret the results, and take actions, i.e., testing and repairing the machine.
  • each one of the CBM module 100 has its own unique ID.
  • the ID is used to uniquely identify the CBM module 100 and the machine within which it is installed.
  • the CBM module 100 may communicate with a server on premise (not on the cloud). This may add an extra layer of security as data belonging to an organization does not leave its premise.
  • Fig. 4 illustrates a method 400 for setting the CBM module 100 in accordance with some embodiments of the present invention.
  • the method 400 comprising the following steps:
  • Step 402 Designing a control circuit to be implemented onto a dedicated space on the main circuit board 200. Designing a host circuit that, in addition to its original purpose of operating the machine, will have space for mounting the CBM module 100 thereto; Step 404: Adding power contacts to be used by the CBM module 100. The power contacts are part of the host circuit and also feed the CBM module 100;
  • Step 406 Optionally adding wiring for monitoring voltages/currents coming from the control circuit.
  • Such wires connect elements in the host circuit to the CBM module 100, and thus, allow continuous examination of multiple machine parameters by monitoring voltages/currents and allow giving output feedback;
  • Step 408 Mounting the CBM module 100 on the main (mother) circuit board 200;
  • Step 410 Connecting power, input and output lines - configuring the input lines of the CBM module 100 and specifying which input lines are used according to the type of data.
  • the software running on the CBM module 100 must recognize that the input/outputs lines are connected in order to use them and take them into account in its operation.
  • the map (location, purpose, modes of operation, and the like) of the contacts will be configured into the software;
  • Step 412 Using an accompanied software or website - login to website/software;
  • Step 414 Filling in details about the machine functions. Entering details of the host machine - this information may affect the way the CBM module 100 will operate at the host machine, e.g., if the host machine is an AC motor, the CBM module 100 may employ motor specific algorithms such as speed detection, synchronous and/or rotational speed, Crest calculation and more;
  • Step 416 Filling in details about the network settings
  • Step 418 Filling in details about the required CBM module operation
  • the CBM module 100 may deploy multiple custom operational modes depending on the application. For instance, the CBM module 100 may use a maintenance mode (count working hours, detect loads, etc.), an anomaly detection mode to detect any abnormal behavior and potential fault development and the like. Multiple modes/capabilities may be available depending on the application.
  • a maintenance mode count working hours, detect loads, etc.
  • an anomaly detection mode to detect any abnormal behavior and potential fault development and the like.
  • Multiple modes/capabilities may be available depending on the application.
  • Step 420 Creating a customized firmware version. After the above details are entered, the software/website creates a firmware version with the requested features. The firmware can then be installed on the CBM module 100 to allow for the requested features; and
  • Step 422 Downloading and installing the new firmware on the CBM module 100 - downloading the customized firmware version and finally installing it on the CBM module 100.
  • the manufacturer when installing the CBM module 100 on the main circuit board 200, the manufacturer configures the CBM module 100 according to its specific use case.
  • graphical representation of the CBM module 100 input/output connectors may be provided via the website or the local software, thus, the manufacturer may easily connect the
  • IB input/output lines from the host circuit to the CBM module 100 to configure the operation of the CBM module 100 accordingly.
  • the manufacturer performs the following steps after installing the CBM module 100 on the main circuit board 200:
  • Examples for such information may include: a.
  • the monthly plan chosen (e.g., detection of up to 10 events per months, detection of maintenance info only, detection of both maintenance info and operational health, anomaly detection mode on/off and the like).
  • b. The login information of the manufacturer (username, password, payment methods and the like).
  • c. The expiry date of the subscription plan of the manufacturer.
  • the software/website creates a unique firmware version (the operating software of the module) for the CBM module 100.
  • the manufacturer downloads the firmware and installs it on the CBM module 100.
  • Fig. 5 is a flowchart 500 of a procedure used for the update of the settings and/or firmware of the CBM module 100 in accordance with some embodiments of the present invention.
  • the flow of Fig. 5 begins in step 502 when the CBM module 100 is connected to the cloud server 302.
  • the CBM module 100 may connect to the cloud server 302, with the network settings and credentials configured by the manufacturer, and may transfer the followings: a. Raw Sensory data; b. Input data received by the host circuit on the main circuit board 200; c. Calculated data and/or statistics created by the CBM module 100 - results of algorithms run locally on the CBM module 100; and d. Operational and health information of the CBM module 100.
  • step 504 the CBM module 100 transmits to the cloud server 302 the current firmware version.
  • Step 506 is a check of whether there is a new available firmware. In case a new firmware is available, the new firmware is downloaded and installed.
  • step 508 if no new firmware is available, step 510 is a check of whether new setting of subscription plan is available. In case new settings of subscription plan are available, the settings are updated in step 512. In case new settings of subscription plan are not available, the operation continues with the current settings in step 514.
  • the subscription plan may affect the operation of the CBM module 100.
  • An additional type of information that may be configured relates to the business contract between the machine manufacturer and the CBM module 100 creator.
  • Such information may include:
  • the monthly plan chosen for instance, detection of up to 10 events per month, detection of maintenance information only, detection of maintenance and operational health, anomaly detection and the like;
  • multiple features and capabilities of the CBM module 100 may be subject to a subscription plan such as a monthly subscription plan.
  • the basic plan may include 3 sensors such as low bandwidth accelerometer, acoustic, and temperature sensors.
  • the Pro plan may include 5 sensors such as low bandwidth accelerometer, high bandwidth accelerometer, acoustic, temperature, and magnetic field sensors.
  • the basic plan may provide monitoring and alert notifications using simple thresholds only (calculated via the CBM module 100 itself or via the cloud software).
  • the Pro plan may include advanced anomaly detection AI algorithms that analyze the collected data. These algorithms can better detect developing problems and “learn” the normal/abnormal behaviors of the machine.
  • the parameters of the algorithms which run on the CBM module 100 may be adjusted and optimized by receiving upgrades from an external server, e.g., the cloud server 302.
  • the CBM module 100 when connected to the cloud server 302, the CBM module 100 first downloads and installs the latest firmware. This ensures the most secured version will be running on the CBM module 100.
  • the system of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instructions which, when executed by the machine, implements some or all of the apparatus, methods, features and functionalities of the invention shown and described herein.
  • the apparatus of the present invention may include, according to certain embodiments of the invention, a program as above which may be written in any conventional programming language, and optionally a machine for executing the program such as but not limited to a general purpose computer which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may wherever suitable operate on signals representative of physical objects or substances.
  • the term "computer” should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, computing system, communication devices, processors (e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.
  • processors e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.
  • DSP digital signal processor
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • software components of the present invention including programs and data may, if desired, be implemented in ROM (read only memory) form including CD-ROMs, EPROMs and EEPROMs, or may be stored in any other suitable typically non-transitory computer-readable medium such as but not limited to disks of various kinds, cards of various kinds and RAMs.
  • ROM read only memory
  • EEPROM electrically erasable programmable read-only memory
  • Components described herein as software may, alternatively, be implemented wholly or partly in hardware, if desired, using conventional techniques.
  • components described herein as hardware may, alternatively, be implemented wholly or partly in software, if desired, using conventional techniques.
  • Any computer-readable or machine-readable media described herein is intended to include non-transitory computer- or machine-readable media.
  • Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any step described herein may be computer- implemented.
  • the invention shown and described herein may include (a) using a computerized method to identify a solution to any of the problems or for any of the objectives described herein, the solution optionally include at least one of a decision, an action, a product, a service or any other information described herein that impacts, in a positive manner, a problem or objectives described herein; and (b) outputting the solution.
  • the scope of the present invention is not limited to structures and functions specifically described herein and is also intended to include devices which have the capacity to yield a structure, or perform a function, described herein, such that even though users of the device may not use the capacity, they are, if they so desire, able to modify the device to obtain the structure or function.
  • a system embodiment is intended to include a corresponding process embodiment.
  • each system embodiment is intended to include a server-centered "view” or client centered “view”, or “view” from any other node of the system, of the entire functionality of the system, computer-readable medium, apparatus, including only those functionalities performed at that server or client or node.

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Abstract

A condition-based monitoring (CBM) module for monitoring machine operation and for detecting anomalies in the machine in real time, a system and a method of using the same. The condition-based monitoring (CBM) module comprises at least: a CPU, memory, at least one onboard sensor, and a communication module. The condition- based monitoring (CBM) module may comprise a processing program for processing sensory data and for detecting and analyzing anomalies in real time. The condition-based monitoring (CBM) module transmit data, raw sensory data and/or processed data, to a cloud/local server for further processing, i.e., for detection and diagnosis of asset anomalies in real time.

Description

CONDITION-BASED MONITORING MODULE, A SYSTEM AND A METHOD OF USING THE SAME
FIELD OF THE INVENTION
The present invention relates to machine maintenance. More specifically, the present invention relates to monitoring the operation of a machine, detecting and diagnosing of machine abnormalities in real time.
BACKGROUND OF THE INVENTION
Condition-based monitoring enables early detection and diagnosis of asset anomalies in real time.
The main point being that the asset condition is assessed under operation with the intention of making conclusions to whether it is in need of maintenance or not and if so at what time does the maintenance actions need to be executed not to suffer a breakdown or malfunction.
Today, there are numerous products that allow monitoring of industrial assets, most include an electric circuit in which sensors are embedded and/or external sensors are connected to. The circuit includes a power source and a communication module to transmit the data outside (usually to the cloud) for further processing. The circuit is always encased inside a tough dedicated box with means of attaching it to existing gear (screws, clamps, etc.).
As the importance and recognition of continuous based monitoring increases, more and more machines will include sensing and monitoring capabilities.
Currently, there are two options for machine manufacturers and/or users:
(a) Integrated CBM -in this option, the machine includes CBM capabilities. This is usually too complicated and costly for manufacturers since the know how and experience of designing a CBM system is unique and mostly different than the capabilities of an industrial manufacturer. In addition, many machines were designed years ago, before the importance of IOT and CBM was recognized. Adding these capabilities to existing machines requires a full redesign, a costly and long procedure. (b) Third party CBM - the use of a third party CBM product with current machines. This solution is usually costly and can not be implemented in each and every machine. Thus, it is an aim of the present invention to provide an improved condition-based monitoring product that is easy to assemble on newly designed machines as well as on existing machines.
An improved condition-based monitoring product that is a standalone product characterized by a compact footprint.
An improved condition-based monitoring product that is extremely sensitive to physical and electrical/magnetic properties of the machine.
SUMMARY OF THE INVENTION
In accordance with some embodiments, there is thus provided, a condition-based monitoring (CBM) module for monitoring machine operation and for detecting anomalies in said machine in real time. The condition-based monitoring (CBM) module comprising at least:
CPU; memory; at least one onboard sensor; and communication module; the condition-based monitoring module is a complete functional unit mounted on a host circuit board of the machine, therefore, receives power from said host circuit board of the machine and optionally receives information/data directly from electric components on the machine electric circuits, said condition-based monitoring module operates independently of the host circuit, said condition-based monitoring module monitors the operation of said machine for detecting and analyzing machine anomalies in real time. Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module further comprising a processing program performing calculations and data analysis and generating machine operational parameters.
Furthermore, in accordance with some embodiments of the present invention, the processing program is comprised of artificial intelligence (AI) models. Furthermore, in accordance with some embodiments of the present invention, the machine operational parameters including rotating speed estimates, statistical profiles, physical profiles, and diagnostic profiles.
Furthermore, in accordance with some embodiments of the present invention, algorithms of said processing program and/or parameters of said algorithms are adjusted and/or optimized by receiving upgrades from an external server.
Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module collecting external and/or internal sensory data and processing the data by said processing program for detecting anomalies. Furthermore, in accordance with some embodiments of the present invention, the at least one onboard sensor detects parameters selected from rotation speed, acceleration, production yield, temperature, magnetic field, acoustic, moisture, air pressure, and mechanical stress.
Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module transmitting said external and/or internal sensory data and/or said processing the data to said server for processing, thus, for detecting anomalies.
Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module does not alter the machine performance. Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module is packaged by “through-hole” or “surface mount” type packaging.
Furthermore, in accordance with some embodiments of the present invention, the packaging is either system in package (SIP) or dual in line package (DIP). Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module operates at voltages between 3-5V. Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) support secure communication (SSL/TLS). Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module support standard industrial communication protocols (TCP/IP, MQTT). Furthermore, in accordance with some embodiments of the present invention, the condition-based monitoring (CBM) module has input and output voltage lines to/from the host circuit board, said input and output voltage lines are selected from:
(a) voltage input lines that indicate the machine operating status,
(b) voltage input lines that indicate health status of the machine,
(c) Voltage input lines that indicate an operational status of the machine, and
(d) voltage input lines that indicate values of sensor(s) installed on the host circuit. Furthermore, in accordance with some embodiments of the present invention, each condition-based monitoring (CBM) module has an ID for identifying said condition- based monitoring (CBM) module and the machine within which it is installed. Furthermore, in accordance with some embodiments of the present invention, there is also provided a system for monitoring machine operation and for detecting anomalies in said machine in real time. The system comprises: the above-described condition-based monitoring (CBM) module and a local/cloud server configured to monitor the machine operation for detecting and diagnosing machine anomalies associated with machine components in real time, wherein the condition-based monitoring (CBM) module transfers sensory data and/or processed data to the server for machine anomaly detection and analysis.
Furthermore, in accordance with some embodiments of the present invention, the server generates machine analytics using features produced and provided by the condition- based monitoring (CBM) module.
Furthermore, in accordance with some embodiments of the present invention, the server detects machine anomalies by comparing statistical distribution of the extracted profiles to saved anomaly type profiles.
Furthermore, in accordance with some embodiments of the present invention, the server transfers updated parameters to algorithms running on the PCB module 100. Furthermore, in accordance with some embodiments of the present invention, there is also provided a method for monitoring machine operation and for detecting anomalies in said machine in real time. The method comprises: mounting the above-mentioned condition-based monitoring (CBM) module on a host circuit board of a machine; receiving power from said host circuit board of the machine; optionally receiving information/data directly from electric components on the machine electric circuits, wherein said condition-based monitoring module operating independently of the host circuit, wherein said condition-based monitoring module monitors the operation of said machine for detecting and analyzing machine anomalies in real time.
Furthermore, in accordance with some embodiments of the present invention, the method further comprising connecting and configuring power input lines and output lines of the condition-based monitoring (CBM).
Furthermore, in accordance with some embodiments of the present invention, the method further comprising adding wiring for monitoring voltages/currents coming from the host circuit.
Furthermore, in accordance with some embodiments of the present invention, the method further comprising downloading and installing updated firmware on the condition-based monitoring (CBM) module.
Furthermore, in accordance with some embodiments of the present invention, downloading and installing updated firmware on the condition-based monitoring (CBM) module comprising:
(a) connecting the condition-based monitoring (CBM) module to a server;
(b) transmitting current firmware version of the condition-based monitoring (CBM) module to the server;
(c) checking whether there is a new available firmware, and downloading and installing the new firmware;
(d) if no new firmware is available, checking whether new setting of subscription plan is available and updating the setting;
(e) if no new setting of subscription plan is available, continuing the operation continues with the current setting. BRIEF DESCRIPTION OF THE FIGURES
Fig. 1 illustrates a condition-based monitoring (CBM) module mountable on a hosting board in accordance with some embodiments of the present invention.
Figs. 2A&B illustrate the condition-based monitoring (CBM) module of the present invention mounted on a hosting main circuit board of a host machine in accordance with some embodiments of the present invention.
Fig. 3 illustrates a monitoring system in accordance with some embodiments of the present invention.
Fig. 4 illustrates a method for setting the CBM module in accordance with some embodiments of the present invention.
Fig. 5 is a flowchart of a procedure used for setting updates of the CBM module in accordance with some embodiments of the present invention.
DETAIFED DESCRIPTION OF THE FIGURES
The present invention provides a preferred solution for machine manufacturers who wish to include condition-based monitoring (CBM) capabilities in their products.
The present invention is of a condition-based monitoring (CBM) module mountable on a host printed circuit.
In accordance with some embodiments of the present invention, machine manufacturers who wish to upgrade their product, i.e., their host machines which may be of various types and models, have to:
1. leave space on the main (mother) printed circuit board (PCB) of the machine.
2. mount the CBM module on the main circuit board.
3. extend power feeding lines to the CBM module.
Fig. 1 illustrates a condition-based monitoring (CBM) module 100 mountable on a hosting board in accordance with some embodiments of the present invention.
In accordance with some embodiments of the present invention, the CBM module 100 is a complete functional unit that includes at least the following modules:
1. CPU 102 2. Memory 104
3. At least one onboard sensor 106
4. Processing program 108
5. Communication module 110
In accordance with some embodiments of the present invention, the condition-based monitoring (CBM) 100 is a complete functional unit comprised of components for collecting external and/or internal data, for processing the data by algorithm(s), for instance, by artificial intelligence (AI) models onboard, and for transmitting data, raw sensory data and/or processed data, to a cloud server (or a local server) for further processing, i.e., for detection and diagnosis of machine anomalies in real time.
In accordance with some embodiments of the present invention, the algorithms of the condition-based monitoring (CBM) 100, for instance, the AI models, are being updated continuously via an external server (local server/cloud server). Such algorithms of the condition-based monitoring (CBM) 100 are continuously updated to improve and increase the capabilities of the condition-based monitoring (CBM) 100.
In accordance with some embodiments of the present invention, the advantage of the CBM module 100 is four-fold:
(a) it is a standalone module that has its own certifications, thus, it can be implemented on an existing main circuit board quickly and easily. The CBM module 100 has no impact on the original design and on the performance of the PCB (host product). The CBM module 100 operates independently of the host circuit and optionally may not get any inputs/output from/to the host circuit board, thus, cannot affect the performance of the host circuit board - any breach to the CBM module (which has remote communication capabilities) cannot harm the host and its performance;
(b) it receives information/data directly from electric components on the machine electric circuits and/or sensors that are mounted directly on the CBM module - due to its position, the CBM module 100 ‘feels’ the same physical (vibrations, sound, temperature) and electrical/magnetic properties that the main board feels; it is, thus, most accurate and representing the host status;
(c) it has a minimal physical foot print. The main board is already encased in a physically protective box, and since the CBM module 100 is mounted on the main board, no additional space/casings and power wirings are required, hence, saving on substantial space and having minimal effect on the product design; and (d) The CBM module 100 can have access to calculated data such as, for instance, machine related performance, operation schedule and the like via host sensors on the main board.
In accordance with some embodiments of the present invention, the CBM module 100 may be packaged using one of many possible integrated circuit packaging types such as system in package (SIP), dual in line package (DIP) and more. The packaging can be of “Through-hole” or “surface mount” types.
In accordance with some embodiments of the present invention, the CBM module 100 may operate at various temperatures and preferably at industrial grade operating temperatures ranging from -40°C to 85°C.
In accordance with some embodiments of the present invention, the CBM module 100 may operate at various voltages and preferably at voltages between 3-5V.
In accordance with some embodiments of the present invention, the CBM module 100 may have a power consumption of less than 1 W.
In accordance with some embodiments of the present invention, the CBM module 100 may support secure communication (SSL/TLS).
In accordance with some embodiments of the present invention, the CBM module 100 may support standard industrial communication protocols (TCP/IP, MQTT).
Figs. 2A&B illustrate the condition-based monitoring (CBM) module 100 of the present invention mounted on a hosting main circuit board 200 of a host machine in accordance with some embodiments of the present invention.
In accordance with some embodiments of the present invention, the CBM module 100 is a complete functional circuit board which operates independently of the host machine control circuit. The CBM module 100 is mounted on the main (mother) circuit board 200 of the machine and receives power from the main circuit board 200, for instance, via contact pads.
The CBM module 100 may include, or may be coupled to, one or more sensors configured to sense information of the machine. Such information/data may indicate the load and functionality of the electronic components, thus, entails the CBM module 100 to detect and diagnose machine anomalies associated with various machine components, such as a motor, a pump, a conveyor belt and the like.
Due to its proximate position, the CBM module 100 ‘feels’ the physical and electrical/magnetic properties of the machine. This allows, for example, to test machine parameters such as vibrations, position, speed, acceleration, energy profiles, magnetic field, temperature, or acoustic information produced by the rotational movement of machine components. Step motor drivers, for instance, consume a lot of power, and hence, release a great amount of heat and emit magnetic fields. Monitoring the heat release and the magnetic field continuously allows effective monitoring of the step motor driver.
In accordance with some embodiments of the present invention, the at least one onboard sensor detects process related parameters such as rotation speed of a motor, acceleration, production yield, temperature at a certain point, magnetic field, acoustic, moisture, air pressure, mechanical stress and the like.
In accordance with some embodiments of the present invention, to avoid possible masking of magnetic fields close to the circuit, the CBM module 100 may be connected to external sensors such as temperature sensors and the like.
In accordance with some embodiments of the present invention, the CBM module 100 receives information/data directly from electric components on the machine electric circuits. For example, the CBM module 100 may get essential information/data points via monitoring electrical elements such as voltage, current, impedance; stress, strain, and the like on the circuit. Such data points are highly essential for detecting and diagnosing machine anomalies.
The CBM module 100 may process the information/data and generate machine operational parameters such as rotating speed estimate, statistical or physical profiles, and diagnostic profiles indicative of various types of faults.
In accordance with some embodiments of the present invention, the CBM module 100 may be programmed to monitor the machine continuously when the machine operates in its normal operating conditions. The CBM module 100 may perform calculations and data analysis onboard before transmitting the data to the cloud server 302. In accordance with some embodiments of the present invention, the CBM module 100 may include machine learning algorithms to identify anomalies in the machine. The machine learning (ML) algorithms may include unsupervised learning to identify multiple machine working states. Such algorithms may include supervised algorithms such as random forest.
The ML algorithms may classify the anomalies according to predefined parameters. The parameters of the algorithms can be updated via a centralized database and may be customized according to the machine type and/or specific demands.
In accordance with some embodiments of the present invention, the CBM module 100 has inputs and output lines from/to the hosting main circuit board 200.
Examples for input lines are:
- Voltage input lines that indicate the machine operating status (on/off/mode 1/mode 2/ mode and the like).
- Voltage input lines that indicate the health status of the machine (healthy/fatal error/electrical fault/mechanical fault and the like).
- Voltage input lines that indicate an operational status of the machine (rotation speed/pressure/injection rate and the like).
- Voltage input lines that indicate the values of a sensor that is installed on the host machine or the host circuit (temperature/pressure/magnetic field/vibrations and the like).
In accordance with some embodiments of the present invention, the CBM module 100 is connected to output lines of the hosting main circuit board 200.
Examples for such output lines may include:
- Calculated maintenance condition of the machine (idle/maintenance is needed/fault and the like).
- Fault indication (no fault/mechanical fault/electrical fault/gear fault/motor drive faults and the like). - CBM status indication - for instance, CBM module network online/offline, error with CBM module and the like.
- Display data - for instance, text to be displayed on the machine screen.
Seen in Fig. 2B, sensor 1 has input and output lines from/to the hosting main circuit board 200 for monitoring the machine speed, sensor 2 has inputs and output lines from/to the hosting main circuit board 200 for detecting the operating mode sensor 3 has input and output lines from/to the hosting main circuit board 200 for monitoring the fault indicator output, sensor 5 has inputs and output lines from/to the hosting main circuit board 200 for monitoring the operating control.
Fig. 3 illustrates a monitoring system 300 in accordance with some embodiments of the present invention.
The monitoring system 300 comprises the CBM module 100 and a cloud server 302 (or a local server) configured to provide a cloud-based service that can be configured to detect and isolate machine anomalies associated with various machine components, such motors, gears, bearings, transmission, or other components.
In accordance with some embodiments of the present invention, the sensory data, received via the CBM module 100, is transferred to the accompanied processing program in CPU 102 which communicates with a processing program 304 in a cloud server 302 via data communication means, communication module 108, such as, for instance, Smart Mesh IP protocol, Narrow band IOT (cellular network), ethemet (local network) and Wi-fi.
Thus, sensory data that is continuously detected via at least one sensor 106, may be processed and then transferred to the cloud server 302 for machine anomaly detection and analysis. The cloud server 302 may be a computing device configured to provide cloud-based service (storage, analytics, maintenance and the like).
A machine analytics may be generated at the cloud 302 using the features produced and provided by the CBM module 100. The machine anomaly may be detected using statistical distributions of a temporal or spectral profile and the like. Anomaly detection may involve comparing the extracted profile, or a statistical distribution of the extracted profile, to saved data containing various anomaly type profiles. The cloud server 302 may transfer updated parameters to the algorithms running on the PCB module 100.
In accordance with some embodiments of the present invention, the user may be able to see the detected anomaly(s) via mobile devices in communication with the cloud such as mobile phones, PCs, and the like. Upon alert notification, the user may view the status, interpret the results, and take actions, i.e., testing and repairing the machine.
In accordance with some embodiments of the present invention, each one of the CBM module 100 has its own unique ID. When uploading data to the cloud server 302, the ID is used to uniquely identify the CBM module 100 and the machine within which it is installed.
In accordance with some embodiments of the present invention, the CBM module 100 may communicate with a server on premise (not on the cloud). This may add an extra layer of security as data belonging to an organization does not leave its premise.
Fig. 4 illustrates a method 400 for setting the CBM module 100 in accordance with some embodiments of the present invention.
The method 400 comprising the following steps:
Step 402: Designing a control circuit to be implemented onto a dedicated space on the main circuit board 200. Designing a host circuit that, in addition to its original purpose of operating the machine, will have space for mounting the CBM module 100 thereto; Step 404: Adding power contacts to be used by the CBM module 100. The power contacts are part of the host circuit and also feed the CBM module 100;
Step 406: Optionally adding wiring for monitoring voltages/currents coming from the control circuit. Such wires connect elements in the host circuit to the CBM module 100, and thus, allow continuous examination of multiple machine parameters by monitoring voltages/currents and allow giving output feedback;
Step 408: Mounting the CBM module 100 on the main (mother) circuit board 200;
Step 410: Connecting power, input and output lines - configuring the input lines of the CBM module 100 and specifying which input lines are used according to the type of data. The software running on the CBM module 100 must recognize that the input/outputs lines are connected in order to use them and take them into account in its operation. The map (location, purpose, modes of operation, and the like) of the contacts will be configured into the software;
Step 412: Using an accompanied software or website - login to website/software;
Step 414: Filling in details about the machine functions. Entering details of the host machine - this information may affect the way the CBM module 100 will operate at the host machine, e.g., if the host machine is an AC motor, the CBM module 100 may employ motor specific algorithms such as speed detection, synchronous and/or rotational speed, Crest calculation and more;
Step 416: Filling in details about the network settings;
These parameters will allow the CBM module 100 to communicate and transmit data to the cloud server 302.
Step 418: Filling in details about the required CBM module operation;
The CBM module 100 may deploy multiple custom operational modes depending on the application. For instance, the CBM module 100 may use a maintenance mode (count working hours, detect loads, etc.), an anomaly detection mode to detect any abnormal behavior and potential fault development and the like. Multiple modes/capabilities may be available depending on the application.
Step 420: Creating a customized firmware version. After the above details are entered, the software/website creates a firmware version with the requested features. The firmware can then be installed on the CBM module 100 to allow for the requested features; and
Step 422: Downloading and installing the new firmware on the CBM module 100 - downloading the customized firmware version and finally installing it on the CBM module 100.
In accordance with some embodiments of the present invention, when installing the CBM module 100 on the main circuit board 200, the manufacturer configures the CBM module 100 according to its specific use case.
In accordance with some embodiments of the present invention, graphical representation of the CBM module 100 input/output connectors may be provided via the website or the local software, thus, the manufacturer may easily connect the
IB input/output lines from the host circuit to the CBM module 100 to configure the operation of the CBM module 100 accordingly.
In accordance with some embodiments of the present invention, to create a firmware for the CBM module 100, the manufacturer performs the following steps after installing the CBM module 100 on the main circuit board 200:
1. Logging into a dedicated website or a local software supplied by the CBM module creator;
2. Filling relevant details about the application, required insights, and the like on the website or software of the manufacturer;
3. Configuring information details (may be configured by the manufacturer).
4. Filling information details of the manufacturer such as but not limited to: a. The type and model of the host machine. b. The physical place of the electric circuit. c. The input lines wired into the CBM module 100. d. The output lines wired from the CBM module 100. e. Special environmental conditions (water, humidity, noise, etc.) f. Network setting to allow the CBM module 100 to connect to the internet - sim card information, wifi details, proxy information and the like.
5. Filling details of the desired operation of the CBM module 100 such as but not limited to: a. Scheduled maintenance conditions (time period, working hours, accumulative loads and the like); b. Operating modes of the host machine (rotating speeds, work schedule, work modes and the like); c. Critical faults that need extra attention (electrical fault, mechanical load, unbalanced base and the like).
6. Filling details of the business contract between the manufacturer and the creator of the CBM module 100.
Examples for such information may include: a. The monthly plan chosen (e.g., detection of up to 10 events per months, detection of maintenance info only, detection of both maintenance info and operational health, anomaly detection mode on/off and the like). b. The login information of the manufacturer (username, password, payment methods and the like). c. The expiry date of the subscription plan of the manufacturer.
After the information has been setup, the software/website creates a unique firmware version (the operating software of the module) for the CBM module 100. The manufacturer downloads the firmware and installs it on the CBM module 100.
Fig. 5 is a flowchart 500 of a procedure used for the update of the settings and/or firmware of the CBM module 100 in accordance with some embodiments of the present invention.
The flow of Fig. 5 begins in step 502 when the CBM module 100 is connected to the cloud server 302. The CBM module 100 may connect to the cloud server 302, with the network settings and credentials configured by the manufacturer, and may transfer the followings: a. Raw Sensory data; b. Input data received by the host circuit on the main circuit board 200; c. Calculated data and/or statistics created by the CBM module 100 - results of algorithms run locally on the CBM module 100; and d. Operational and health information of the CBM module 100.
In step 504, the CBM module 100 transmits to the cloud server 302 the current firmware version. Step 506 is a check of whether there is a new available firmware. In case a new firmware is available, the new firmware is downloaded and installed. In step 508, if no new firmware is available, step 510 is a check of whether new setting of subscription plan is available. In case new settings of subscription plan are available, the settings are updated in step 512. In case new settings of subscription plan are not available, the operation continues with the current settings in step 514. In accordance with some embodiments of the present invention, the subscription plan may affect the operation of the CBM module 100.
An additional type of information that may be configured relates to the business contract between the machine manufacturer and the CBM module 100 creator.
Such information may include:
(a) The monthly plan chosen (for instance, detection of up to 10 events per month, detection of maintenance information only, detection of maintenance and operational health, anomaly detection and the like);
(b) The login information of the manufacturer (username, password, payment methods, and the like);
(c) The expiry date of the subscription plan;
In accordance with some embodiments of the present invention, multiple features and capabilities of the CBM module 100 may be subject to a subscription plan such as a monthly subscription plan. For instance, the basic plan may include 3 sensors such as low bandwidth accelerometer, acoustic, and temperature sensors.
The Pro plan may include 5 sensors such as low bandwidth accelerometer, high bandwidth accelerometer, acoustic, temperature, and magnetic field sensors.
In another example, the basic plan may provide monitoring and alert notifications using simple thresholds only (calculated via the CBM module 100 itself or via the cloud software).
The Pro plan may include advanced anomaly detection AI algorithms that analyze the collected data. These algorithms can better detect developing problems and “learn” the normal/abnormal behaviors of the machine.
In accordance with some embodiments of the present invention, it may be possible to enable/disable features/functions of the CBM module 100 as well as features/functions/capabilities of the cloud software.
In accordance with some embodiments of the present invention, the parameters of the algorithms which run on the CBM module 100 may be adjusted and optimized by receiving upgrades from an external server, e.g., the cloud server 302. In accordance with some embodiments of the present invention, when connected to the cloud server 302, the CBM module 100 first downloads and installs the latest firmware. This ensures the most secured version will be running on the CBM module 100.
The system of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instructions which, when executed by the machine, implements some or all of the apparatus, methods, features and functionalities of the invention shown and described herein. Alternatively or in addition, the apparatus of the present invention may include, according to certain embodiments of the invention, a program as above which may be written in any conventional programming language, and optionally a machine for executing the program such as but not limited to a general purpose computer which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may wherever suitable operate on signals representative of physical objects or substances.
Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions, utilizing terms such as, "processing", "computing", "estimating", "selecting", "ranking", "grading", "calculating", "determining", "generating", "reassessing", "classifying", "generating", "producing", "stereo-matching", "registering", "detecting", "associating", "superimposing", "obtaining" or the like, refer to the action and/or processes of a computer or computing system, or processor or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories, into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices. The term "computer" should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, computing system, communication devices, processors (e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.
The present invention may be described, merely for clarity, in terms of terminology specific to particular programming languages, operating systems, browsers, system versions, individual products, and the like. It will be appreciated that this terminology is intended to convey general principles of operation clearly and briefly, by way of example, and is not intended to limit the scope of the invention to any particular programming language, operating system, browser, system version, or individual product.
It is appreciated that software components of the present invention including programs and data may, if desired, be implemented in ROM (read only memory) form including CD-ROMs, EPROMs and EEPROMs, or may be stored in any other suitable typically non-transitory computer-readable medium such as but not limited to disks of various kinds, cards of various kinds and RAMs. Components described herein as software may, alternatively, be implemented wholly or partly in hardware, if desired, using conventional techniques. Conversely, components described herein as hardware may, alternatively, be implemented wholly or partly in software, if desired, using conventional techniques.
Included in the scope of the present invention, inter alia, are electromagnetic signals carrying computer-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; machine-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; program storage devices readable by machine, tangibly embodying a program of instructions executable by the machine to perform any or all of the steps of any of the methods shown and described herein, in any suitable order; a computer program product comprising a computer useable medium having computer readable program code, such as executable code, having embodied therein, and/or including computer readable program code for performing, any or all of the steps of any of the methods shown and described herein, in any suitable order; any technical effects brought about by any or all of the steps of any of the methods shown and described herein, when performed in any suitable order; any suitable apparatus or device or combination of such, programmed to perform, alone or in combination, any or all of the steps of any of the methods shown and described herein, in any suitable order; electronic devices each including a processor and a cooperating input device and/or output device and operative to perform in software any steps shown and described herein; information storage devices or physical records, such as disks or hard drives, causing a computer or other device to be configured so as to carry out any or all of the steps of any of the methods shown and described herein, in any suitable order; a program pre-stored e.g. in memory or on an information network such as the Internet, before or after being downloaded, which embodies any or all of the steps of any of the methods shown and described herein, in any suitable order, and the method of uploading or downloading such, and a system including server/s and/or client/s for using such; and hardware which performs any or all of the steps of any of the methods shown and described herein, in any suitable order, either alone or in conjunction with software. Any computer-readable or machine-readable media described herein is intended to include non-transitory computer- or machine-readable media.
Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any step described herein may be computer- implemented. The invention shown and described herein may include (a) using a computerized method to identify a solution to any of the problems or for any of the objectives described herein, the solution optionally include at least one of a decision, an action, a product, a service or any other information described herein that impacts, in a positive manner, a problem or objectives described herein; and (b) outputting the solution.
The scope of the present invention is not limited to structures and functions specifically described herein and is also intended to include devices which have the capacity to yield a structure, or perform a function, described herein, such that even though users of the device may not use the capacity, they are, if they so desire, able to modify the device to obtain the structure or function.
Features of the present invention which are described in the context of separate embodiments may also be provided in combination in a single embodiment. For example, a system embodiment is intended to include a corresponding process embodiment. Also, each system embodiment is intended to include a server-centered "view" or client centered "view", or "view" from any other node of the system, of the entire functionality of the system, computer-readable medium, apparatus, including only those functionalities performed at that server or client or node.

Claims

CLAIMS:
1. A condition-based monitoring (CBM) module for monitoring machine operation and for detecting anomalies in said machine in real time comprising at least:
CPU; memory; at least one onboard sensor; and communication module; said condition-based monitoring module is a complete functional unit mounted on a host circuit board of the machine, therefore, receives power from said host circuit board of the machine and optionally receives information/data directly from electric components on the machine electric circuits, said condition-based monitoring module operates independently of the host circuit, said condition- based monitoring module monitors the operation of said machine for detecting and analyzing machine anomalies in real time.
2. The condition-based monitoring (CBM) module of claim 1 further comprising a processing program performing calculations and data analysis and generating machine operational parameters.
3. The condition-based monitoring (CBM) module of claim 2, wherein said processing program is comprised of artificial intelligence (AI) models.
4. The condition-based monitoring (CBM) module of claim 1, wherein said machine operational parameters including rotating speed estimates, statistical profiles, physical profiles, and diagnostic profiles.
5. The condition-based monitoring (CBM) module of any one of claims 2-4, wherein algorithms of said processing program and/or parameters of said algorithms are adjusted and/or optimized by receiving upgrades from an external server.
6. The condition-based monitoring (CBM) module of any one of claims 2-4, wherein said condition-based monitoring (CBM) module collecting external and/or internal sensory data and processing the data by said processing program for detecting anomalies.
7. The condition-based monitoring (CBM) module of any one of claims 1-6, wherein said at least one onboard sensor detects parameters selected from rotation speed, acceleration, production yield, temperature, magnetic field, acoustic, moisture, air pressure, and mechanical stress.
8. The condition-based monitoring (CBM) module of any one of claims 2-7, wherein said condition-based monitoring (CBM) module transmitting said external and/or internal sensory data and/or said processing the data to said server for processing, thus, for detecting anomalies.
9. The condition-based monitoring (CBM) module of any one of claims 1-8, wherein said condition-based monitoring (CBM) module does not alter the machine performance.
10. The condition-based monitoring (CBM) module of any one of claims 1-9, wherein said condition-based monitoring (CBM) module is packaged by “through-hole” or “surface mount” type packaging.
11. The condition-based monitoring (CBM) module of any one of claims 1-10, wherein said packaging is either system in package (SIP) or dual in line package (DIP).
12. The condition-based monitoring (CBM) module of any one of claims 1-11, wherein said condition-based monitoring (CBM) module operates at voltages between 3-5V.
13. The condition-based monitoring (CBM) module of any one of claims 1-12, wherein said condition-based monitoring (CBM) support secure communication (SSL/TLS).
14. The condition-based monitoring (CBM) module of any one of claims 1-13, wherein said condition-based monitoring (CBM) module support standard industrial communication protocols (TCP/IP, MQTT).
15. The condition-based monitoring (CBM) module of any one of claims 1-14, wherein said condition-based monitoring (CBM) module has input and output voltage lines to/from the host circuit board, said input and output voltage lines are selected from:
(a) voltage input lines that indicate the machine operating status, (b) voltage input lines that indicate health status of the machine,
(c) Voltage input lines that indicate an operational status of the machine, and
(d) voltage input lines that indicate values of sensor(s) installed on the host circuit.
16. The condition-based monitoring (CBM) module of any one of claims 1-15, wherein each condition-based monitoring (CBM) module has an ID for identifying said condition-based monitoring (CBM) module and the machine within which it is installed.
17. A system for monitoring machine operation and for detecting anomalies in said machine in real time comprising: the condition-based monitoring (CBM) module of any one of claims 1-16 and a local/cloud server configured to monitor the machine operation for detecting and diagnosing machine anomalies associated with machine components in real time, wherein the condition-based monitoring (CBM) module transfers sensory data and/or processed data to the server for machine anomaly detection and analysis.
18. The system of claim 17, wherein the server generates machine analytics using features produced and provided by the condition-based monitoring (CBM) module.
19. The system of any one of claims 17-18, wherein the server detects machine anomalies by comparing statistical distribution of the extracted profiles to saved anomaly type profiles.
20. The system of any one of claims 17-21, wherein the server transfers updated parameters to algorithms running on the PCB module 100.
21. A method for monitoring machine operation and for detecting anomalies in said machine in real time comprising: mounting the condition-based monitoring (CBM) module of any one of claims
1-16 on a host circuit board of a machine; receiving power from said host circuit board of the machine; optionally receiving information/data directly from electric components on the machine electric circuits, wherein said condition-based monitoring module operating independently of the host circuit, wherein said condition-based monitoring module monitors the operation of said machine for detecting and analyzing machine anomalies in real time.
22. The method of claim 21 further comprising connecting and configuring power input lines and output lines of the condition-based monitoring (CBM).
23. The method of claim 22, further comprising adding wiring for monitoring voltages/currents coming from the host circuit.
24. The method of claim 21, further comprising downloading and installing updated firmware on the condition-based monitoring (CBM) module.
25. The method of claim 24, wherein downloading and installing updated firmware on the condition-based monitoring (CBM) module comprising:
(a) connecting the condition-based monitoring (CBM) module to a server;
(b) transmitting current firmware version of the condition-based monitoring (CBM) module to the server;
(c) checking whether there is a new available firmware, and downloading and installing the new firmware;
(d) if no new firmware is available, checking whether new setting of subscription plan is available and updating the setting;
(e) if no new setting of subscription plan is available, continuing the operation continues with the current setting.
PCT/IL2022/050301 2021-03-16 2022-03-16 Condition-based monitoring module, a system and a method of using the same WO2022195594A1 (en)

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JP2004257955A (en) * 2003-02-27 2004-09-16 Mitsubishi Electric Corp Remote status monitoring system
EP2749977A1 (en) * 2011-10-12 2014-07-02 Yanmar Co., Ltd. Remote monitoring terminal device for traveling work machine or ship
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