CN117745256A - Predictive maintenance method and system for hydraulic oil source equipment - Google Patents
Predictive maintenance method and system for hydraulic oil source equipment Download PDFInfo
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- 238000012423 maintenance Methods 0.000 title claims abstract description 140
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Abstract
The invention provides a predictive maintenance method and a predictive maintenance system for hydraulic oil source equipment, wherein the predictive maintenance method comprises the following steps: analyzing failure modes of key components of hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components; determining equipment characterization points of important physical characteristics, and setting corresponding sensors at the equipment characterization points; determining a transmission mode of data acquired by a corresponding sensor, and storing and processing the acquired data; constructing a predictive maintenance model of equipment faults, and analyzing the processed data to obtain the health status condition and fault diagnosis result of the equipment; and automatically forming a predictive maintenance mode and a plan according to the diagnosis result, and early warning a user to perform related maintenance work. In the aspect of maintenance of high-pressure large-flow hydraulic oil source equipment, the equipment maintenance efficiency and the production economic benefit of enterprises are improved, and meanwhile, the threshold of equipment maintenance is reduced. Let the device itself "tell" its own problem, rather than by a maintenance person.
Description
Technical Field
The invention relates to the technical field of detection and maintenance, in particular to a predictive maintenance method and system for hydraulic oil source equipment. In particular to state monitoring, fault diagnosis and maintenance advice of high-pressure high-flow hydraulic oil source equipment.
Background
The hydraulic oil source equipment has the characteristics of high power density, long service life, easiness in automation and the like, and is widely applied to various fields. Along with the increasing automation level of equipment, the complexity and the association degree of the structure and the system are also increased, and once a fault occurs, a light person can cause the equipment to be damaged in function and stop the production line, so that huge economic loss and environmental pollution are caused; if the weight is heavy, the casualties can be caused, and serious production safety accidents are caused.
Maintenance work of the apparatus is necessary in order for the apparatus to be able to operate stably and efficiently. At present, in the hydraulic field, a mode of preventive maintenance and planned overhaul is commonly adopted. Although this method is an active maintenance method, it has the problems of a large number of maintenance items, high maintenance cost, and excessive local maintenance, and relies on the experience of maintenance personnel. For high-pressure large-flow hydraulic oil source equipment, the structure and the system complexity of the equipment make maintenance and judgment more difficult. And preventive maintenance is to carry out corresponding measure maintenance aiming at common faults of equipment, the hidden danger of the equipment is unpredictable, and the equipment is suddenly broken down due to the problems of poor maintenance and the like, so that the maintenance period is long.
In contrast to preventative maintenance, predictive maintenance is not based on a fixed maintenance cycle, but rather is a demand-oriented based maintenance operation using continuously collected measurement data of field devices and corresponding data evaluation methods. Predictive maintenance records critical parameter information for equipment operation that may indicate that machine operation is problematic for a long period of time before actual damage occurs. Therefore, the effective preventive maintenance of the equipment has better maintenance pertinence and accuracy, and the hidden trouble of the equipment is reduced.
In view of the foregoing, there is a need in the marketplace for a predictive maintenance method and system that can maintain low cost and efficient hydraulic oil source equipment.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a predictive maintenance method and a predictive maintenance system for hydraulic oil source equipment.
The predictive maintenance method for the hydraulic oil source equipment provided by the invention comprises the following steps:
step S1: analyzing failure modes of key components of hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components;
step S2: determining equipment characterization points of the important physical characteristics, and setting corresponding sensors at the equipment characterization points;
step S3: determining a transmission mode of data acquired by a corresponding sensor, and storing and processing the acquired data;
step S4: constructing a predictive maintenance model of equipment faults, and analyzing the processed data to obtain the health status condition and fault diagnosis result of the equipment;
step S5: and automatically forming a predictive maintenance mode and a predictive maintenance plan according to the diagnosis result, and early warning a user to perform related maintenance work.
Preferably, the key components comprise a motor-pump assembly, an oil source assembly and a hydraulic control element of the hydraulic oil source device;
the failure mode is a fault that the equipment cannot normally operate due to various factors;
the important physical characteristics comprise temperature, vibration, rotating speed, pressure, flow, liquid level and oil cleanliness;
the sensor comprises a temperature sensor, an acceleration sensor, a rotation speed sensor, a pressure sensor, a flow sensor, a liquid level sensor and an online granularity sensor.
Preferably, the temperature sensors are arranged at the oil tank end of the oil source assembly and the engine oil chamber of the motor-pump assembly and are used for monitoring the oil temperature in the oil tank and the temperature condition of the speed reducer engine oil in the pump unit;
the acceleration sensor is arranged at one end of the rigid body of the coupler of the motor-pump assembly, which is close to the rigid assembly of the pump assembly, and is used for monitoring the integral vibration condition of the motor-pump assembly;
the rotating speed sensor is arranged at the rotor end of the motor and used for monitoring the rotation condition of the motor;
the pressure sensor is arranged at the rear end of the hydraulic control element and used for monitoring the pressure pulsation condition of the system, and is also arranged at the two ends of the high-pressure filter of the equipment and used for monitoring the pollution condition and the whole abrasion condition of the oil filter;
the flow sensor is provided with an equipment external leakage pipeline for monitoring the leakage condition of the system;
the liquid level sensor is arranged in the oil tank of the oil source assembly and is used for monitoring the oil storage condition of the system;
the online granularity sensor is arranged at the rear end of the system high-pressure oil filter pipeline and is used for monitoring the cleanliness of the system oil.
Preferably, the data collected in the step S3 include online operation data, failure experience data and life prediction theory data of the hydraulic oil source device;
the online operation data comprise sensor data of the hydraulic oil source equipment and external sensor data with increased predictive maintenance, and the online operation data are collected to an upper computer or a server through a communication interface and a TCP/IP protocol and are used for analyzing real-time conditions of the equipment;
the failure experience data and the life prediction theoretical data are obtained through calculation of actual failure conditions and theoretical life of equipment and serve as the basis of a predictive maintenance model.
Preferably, processing the acquired data in step S3 includes:
for the acceleration sensor, firstly, the noise of the acquired original signal is eliminated by utilizing Walsh transformation and the like, then envelope demodulation is carried out by utilizing Teager and the like, finally, the Walsh coefficient is filtered by utilizing a soft threshold value, and the Walsh inverse transformation is carried out, so that the data to be analyzed finally are obtained.
Preferably, the predictive maintenance model is constructed based on failure experience data of the equipment and life prediction theory data of the equipment, and the operation condition and the health state of the equipment are judged through the change, trend and threshold value of the extracted physical characteristics of the failure mode of the equipment.
The predictive maintenance system for hydraulic oil source equipment provided by the invention comprises the following components:
module M1: analyzing failure modes of key components of hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components;
module M2: determining equipment characterization points of the important physical characteristics, and setting corresponding sensors at the equipment characterization points;
module M3: determining a transmission mode of data acquired by a corresponding sensor, and storing and processing the acquired data;
module M4: constructing a predictive maintenance model of equipment faults, and analyzing the processed data to obtain the health status condition and fault diagnosis result of the equipment;
module M5: and automatically forming a predictive maintenance mode and a predictive maintenance plan according to the diagnosis result, and early warning a user to perform related maintenance work.
Preferably, the key components comprise a motor-pump assembly, an oil source assembly and a hydraulic control element of the hydraulic oil source device;
the failure mode is a fault that the equipment cannot normally operate due to various factors;
the important physical characteristics comprise temperature, vibration, rotating speed, pressure, flow, liquid level and oil cleanliness;
the sensor comprises a temperature sensor, an acceleration sensor, a rotation speed sensor, a pressure sensor, a flow sensor, a liquid level sensor and an online granularity sensor.
Preferably, the temperature sensors are arranged at the oil tank end of the oil source assembly and the engine oil chamber of the motor-pump assembly and are used for monitoring the oil temperature in the oil tank and the temperature condition of the speed reducer engine oil in the pump unit;
the acceleration sensor is arranged at one end of the rigid body of the coupler of the motor-pump assembly, which is close to the rigid assembly of the pump assembly, and is used for monitoring the integral vibration condition of the motor-pump assembly;
the rotating speed sensor is arranged at the rotor end of the motor and used for monitoring the rotation condition of the motor;
the pressure sensor is arranged at the rear end of the hydraulic control element and used for monitoring the pressure pulsation condition of the system, and is also arranged at the two ends of the high-pressure filter of the equipment and used for monitoring the pollution condition and the whole abrasion condition of the oil filter;
the flow sensor is provided with an equipment external leakage pipeline for monitoring the leakage condition of the system;
the liquid level sensor is arranged in the oil tank of the oil source assembly and is used for monitoring the oil storage condition of the system;
the online granularity sensor is arranged at the rear end of the system high-pressure oil filter pipeline and is used for monitoring the cleanliness of the system oil.
Preferably, the data collected in the module M3 include on-line operation data, failure experience data and life prediction theory data of the hydraulic oil source device;
the online operation data comprise sensor data of the hydraulic oil source equipment and external sensor data with increased predictive maintenance, and the online operation data are collected to an upper computer or a server through a communication interface and a TCP/IP protocol and are used for analyzing real-time conditions of the equipment;
the failure experience data and the life prediction theoretical data are obtained through calculation of actual failure conditions and theoretical life of equipment and serve as the basis of a predictive maintenance model.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, various sensors are arranged on the equipment, the real-time states of key parts and vulnerable parts of the equipment are judged by a multi-source data integration technical method, and a predictive maintenance model is established by a machine learning and statistical learning method, so that the on-line monitoring, preventive diagnosis and maintenance suggestion of the equipment are realized. In the aspect of maintenance of high-pressure large-flow hydraulic oil source equipment, the equipment maintenance efficiency and the production economic benefit of enterprises are improved, and meanwhile, the threshold of equipment maintenance is reduced. Let the device itself "tell" its own problem, rather than by a maintenance person.
Meanwhile, when degradation trend occurs on one aspect of the equipment, fault early warning can be carried out, a user is recommended to carry out corresponding maintenance operation, the production and maintenance cost of an enterprise is reduced, the utilization rate and the competitiveness of the equipment are improved, and the production economic benefit of the enterprise is enlarged.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of the physical characteristics of key components of the apparatus of the present invention;
FIG. 3 is a schematic diagram of a maintenance plan and multi-source data relationship of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
According to the predictive maintenance method for hydraulic oil source equipment provided by the invention, as shown in fig. 1, the predictive maintenance method comprises the following steps:
step S1: and analyzing failure modes of key components of the high-pressure high-flow hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components. Critical components include the motor-pump assembly, the oil supply assembly, and the hydraulic control elements of the apparatus, and failure modes thereof include failures that cause the apparatus to fail to function properly due to various factors. Important physical characteristics of the device include temperature, vibration, rotational speed, pressure, flow, liquid level, oil cleanliness, etc., and in correspondence thereto, the sensors include temperature sensors (infrared sensors), acceleration sensors, rotational speed sensors, pressure sensors, flow sensors, liquid level sensors, on-line granularity sensors, which are configured as an on-line basis data source for predictive maintenance of the device.
Step S2: and determining equipment characterization points of the important physical characteristics, and setting corresponding sensors at the equipment characterization points. The temperature sensor is mainly arranged at the oil tank end of the oil source assembly and the engine oil chamber of the motor-pump assembly and is used for monitoring the oil temperature in the oil tank and the temperature condition of the speed reducer engine oil in the pump unit; the acceleration sensor is mainly arranged at one end of the rigid body of the coupler of the motor-pump assembly, which is close to the rigid assembly of the pump assembly, and is used for monitoring the integral vibration condition of the motor-pump assembly; the rotating speed sensor is mainly arranged at the rotor end of the motor and is used for monitoring the rotating condition of the motor; the pressure sensor is mainly arranged at the rear end of the hydraulic control element and is used for monitoring the pressure pulsation condition of the system, and is also arranged at the two ends of the high-pressure filter of the equipment and is used for monitoring the pollution condition of the oil filter and the abrasion condition of the whole system, particularly the pump group; the flow sensor is mainly provided with an equipment external leakage pipeline and is used for monitoring the leakage condition of the system; the liquid level sensor is mainly arranged in an oil tank of the oil source assembly and is used for monitoring the oil storage condition of the system; the on-line granularity sensor is arranged at the rear end of the system high-pressure oil filter pipeline and is used for monitoring the cleanliness of the system oil.
The real-time state of a certain key component or vulnerable part of the equipment is characterized by data integration of single-type or multi-type sensors and multi-mode data increment clustering fusion, and the real-time state is used as a diagnosis basis for predictive maintenance of the equipment. For example, the coupling of the liquid level sensor data and the flow sensor data of the device may characterize an abnormal leakage condition of the device as a whole, the coupling of the temperature sensor data and the acceleration sensor data of the device may characterize an abnormal wear condition of the decelerator of the pump set of the device, and so on.
Step S3: and determining a transmission mode of data acquired by the corresponding sensor, and storing and processing the acquired data. The collected data comprise on-line operation data of the equipment, fault experience data of the equipment and life prediction theory data of the equipment. The online operation data of the equipment comprises important physical characteristics of the equipment, specifically, sensor data of the equipment and external sensor data with increased predictive maintenance, and the data are collected to an upper computer or a server through a communication interface and a TCP/IP protocol and are used for analyzing the real-time situation of the equipment; the failure experience data of the equipment and the life prediction theoretical data of the equipment are obtained through calculation of the actual failure condition and the theoretical life of the equipment, and are mainly used as the basis of a predictive maintenance model.
Processing the acquired data includes: for the acceleration sensor, firstly, the noise of the acquired original signal is eliminated by utilizing Walsh transformation and the like, then envelope demodulation is carried out by utilizing Teager and the like, finally, the Walsh coefficient is filtered by utilizing a soft threshold value, and the Walsh inverse transformation is carried out, so that the data to be analyzed finally are obtained.
Step S4: and constructing a predictive maintenance model of equipment faults, and analyzing the processed data to obtain the health status condition and fault diagnosis result of the equipment. The predictive maintenance model of the equipment is constructed based on the failure experience data of the equipment and the life prediction theory data of the equipment, and the running condition and the health state of the equipment are judged through the change, trend and threshold value of the extracted physical characteristics of the failure mode of the equipment. The collected and processed sensor data are analyzed in a predictive maintenance model, and the state of the equipment at the moment is judged by using an improved support vector machine, machine learning and other algorithms. Meanwhile, the data are also used for enriching a database of the model, and the accuracy of the model is continuously corrected by comparing and analyzing in the dimension of the time domain, so that the accuracy of model prediction is improved.
Step S5: and automatically forming a predictive maintenance mode and a plan according to the diagnosis result, and early warning a user to perform related maintenance work. When the state of the predictive maintenance model diagnosis equipment is good, the system prompts to be normal, and maintenance operation is not required by a user; when the predictive maintenance model diagnoses the abnormal state of the equipment, the system alerts, makes a maintenance method and a maintenance plan according to the abnormal situation, and sends the maintenance method and the maintenance plan to a user for on-site maintenance operation.
The invention provides a predictive maintenance method of high-pressure high-flow hydraulic oil source equipment, which relates to a multi-source data correlation analysis method of the hydraulic oil source equipment, and is aimed at a fault mechanism, a fault mode and a monitoring method of a part body of the key part of the hydraulic oil source equipment, and the targets of state monitoring, fault analysis, life prediction, intelligent operation and maintenance and the like of the key part of the hydraulic oil source equipment are realized by adopting a multi-source data integration technology; combining machine learning and statistical learning methods, establishing a health state diagnosis model of a hydraulic oil source key component based on real-time health state monitoring, and researching a predictive maintenance model; and establishing an online operation and maintenance service system of the hydraulic oil source equipment to realize online monitoring, fault diagnosis and maintenance suggestion.
The invention aims to enable a user to know the health state of the current equipment through extraction, on-line state monitoring and equipment prediction diagnosis of physical features of key parts of the equipment, and simultaneously, when a degradation trend occurs on one aspect of the equipment, fault early warning can be carried out, the user is recommended to carry out corresponding maintenance operation, the production and maintenance cost of enterprises is reduced, the utilization rate and the competitiveness of the equipment are improved, and the production economic benefit of the enterprises is enlarged.
Further, the predictive maintenance method for the hydraulic oil source apparatus of the present invention is specifically described below with reference to fig. 1 to 3:
the invention as shown in fig. 1 comprises important physical characteristics for identifying the failure of a key component, arranging sensors at the characterization points of the important physical characteristics, collecting, transmitting and processing sensor data, analyzing and diagnosing the data, predicting and forming maintenance strategies and suggestions.
First, important physical features of critical component failure are identified as by step S1. And analyzing failure modes of key components of the hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components.
The method for identifying the important physical characteristics of the failure of the key component is as follows: the key components of the equipment are firstly screened out, and for high-pressure high-flow hydraulic oil source equipment, the key components are generally a motor-pump assembly, an oil source assembly and a hydraulic control element. In the forward direction, the failure mode of the equipment is analyzed through an EFEMA theory, and important physical characteristics affecting the operation of key parts are combed; and reversely, carding common fault characteristic points of the equipment through daily maintenance, maintenance modes and experience of the equipment, and finally forming a physical characteristic relation diagram of key parts of the equipment.
Then, a sensor is set at the important physical feature characterization point, as by step S2. The location of the sensor is important and determines whether the acquired data is in line with the actual state of the device. The method for setting the position of the sensor is as follows: the sensitivity of sensor change is tested by setting sensors at different points in the setting area, and in general, the higher the sensitivity of sensor change is, the more the data collected by the sensor can represent the real state of the equipment. The higher the sample size tested, the more accurate the sensor setup. Meanwhile, through the equipment structure principle and experience judgment, the method can help to find a more accurate sensor installation position. As shown in fig. 2, the key components of the hydraulic oil source device in the invention comprise a motor-pump assembly, an oil source assembly and a control element, wherein the physical characteristics of the motor-pump assembly comprise rotating speed, vibration and temperature, the physical characteristics of the oil source assembly comprise temperature, liquid level and pressure, and the physical characteristics of the control element comprise flow and oil granularity.
Next, the sensor data is collected, transmitted, and processed, as by step S3. Sensor data enters the acquisition card or the upper computer through an acquisition protocol, and is transmitted through a TCP/IP or other remote communication protocol modes. In the data processing, for signals such as general temperature, analog quantity is required to be converted into digital quantity, and the data to be analyzed is obtained through a Kalman filtering signal noise reduction method; for signals such as vibration, firstly, the noise of the acquired original signals is eliminated by utilizing Walsh transformation and the like, then envelope demodulation is carried out by utilizing Teager and the like, finally, the Walsh coefficient is filtered by utilizing a soft threshold value, and the Walsh inverse transformation is carried out, so that the data to be analyzed finally are obtained.
Then, the data is analyzed and the diagnosis is predicted, step S4. Data analysis and diagnostic prediction require predictive maintenance models to be built. The model is built based on the failure experience data of the equipment and life prediction theory data of the equipment, and the failure characteristics of the equipment are often not the change of single factors, but a set of multiple factors. And reversely deducing the failure mode of the key component of the equipment through the multi-source data set, extracting the change, trend and threshold value of the multi-source data when the key component of the equipment is deteriorated through a mode of combining a physical experiment, a simulation experiment and theoretical calculation, and establishing a predictive maintenance model. And analyzing and judging the data by combining a machine learning method and a statistical learning method. Meanwhile, the data are also used for enriching a database of the model, and the accuracy of the model is continuously corrected by comparing and analyzing in the dimension of the time domain, so that the accuracy of model prediction is improved. Wherein a maintenance plan and multi-source data relationship diagram is shown in fig. 3.
Finally, maintenance strategies and suggestions are formed as by step S5. The maintenance strategy and the proposal are derived from analysis of the model multi-source data set to judge the failure mode of the key parts of the equipment, predict the failure time point and establish a corresponding maintenance method and a corresponding plan through the failure mode. For example, abnormal changes of liquid level sensor data and flow sensor data of the equipment represent that abnormal leakage occurs in the whole equipment, and corresponding equipment pipeline overhaul and maintenance plans can be set at the moment, so that large-scale leakage accidents of the equipment are avoided; abnormal changes of the temperature sensor data and the acceleration sensor data of the equipment can represent abnormal wear of a speed reducer of the pump set of the equipment, and corresponding maintenance and oil replacement maintenance plans of the pump set can be set at the moment to avoid abnormal shutdown faults of the pump set of the equipment. When the predictive maintenance model diagnoses the abnormal state of the equipment, the system alerts, makes a maintenance method and a maintenance plan according to the abnormal situation, and sends the maintenance method and the maintenance plan to a user for on-site maintenance operation.
The invention also provides a predictive maintenance system of the hydraulic oil source equipment, which can be realized by executing the flow steps of the predictive maintenance method of the hydraulic oil source equipment, namely, a person skilled in the art can understand the predictive maintenance method of the hydraulic oil source equipment as a preferred implementation mode of the predictive maintenance system of the hydraulic oil source equipment.
The predictive maintenance system for hydraulic oil source equipment provided by the invention comprises the following components:
module M1: and analyzing failure modes of key components of the hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components. The key components comprise a motor-pump assembly, an oil source assembly and a hydraulic control element of the hydraulic oil source equipment, the failure mode is a fault that the equipment cannot normally operate due to various factors, important physical characteristics comprise temperature, vibration, rotating speed, pressure, flow, liquid level and oil cleanliness, and the sensor comprises a temperature sensor, an acceleration sensor, a rotating speed sensor, a pressure sensor, a flow sensor, a liquid level sensor and an online granularity sensor.
Module M2: and determining equipment characterization points of the important physical characteristics, and setting corresponding sensors at the equipment characterization points. The temperature sensor is arranged at the oil tank end of the oil source assembly and the engine oil chamber of the motor-pump assembly and is used for monitoring the oil temperature in the oil tank and the temperature condition of the speed reducer engine oil in the pump group. The acceleration sensor is arranged at one end of the rigid body of the coupling of the motor-pump assembly, which is close to the rigid assembly of the pump assembly, and is used for monitoring the integral vibration condition of the motor-pump assembly, and the rotating speed sensor is arranged at the motor rotor end and is used for monitoring the rotation condition of the motor. The pressure sensor is arranged at the rear end of the hydraulic control element and used for monitoring the pressure pulsation condition of the system, and meanwhile, the pressure sensor is also arranged at the two ends of the high-pressure filter of the equipment and used for monitoring the pollution condition and the whole abrasion condition of the oil filter. The flow sensor is provided with an external leakage pipeline for monitoring the leakage condition of the system. The liquid level sensor is arranged in the oil tank of the oil source assembly and used for monitoring the oil storage condition of the system. The on-line granularity sensor is arranged at the rear end of the system high-pressure oil filter pipeline and is used for monitoring the cleanliness of the system oil.
Module M3: and determining a transmission mode of data acquired by the corresponding sensor, and storing and processing the acquired data. The data collected in the module M3 comprise on-line operation data, fault experience data and life prediction theory data of the hydraulic oil source equipment. The online operation data comprise sensor data of the hydraulic oil source equipment and external sensor data with increased predictive maintenance, and the online operation data are collected to an upper computer or a server through a communication interface and a TCP/IP protocol and are used for analyzing the real-time situation of the equipment. The failure experience data and the life prediction theory data are obtained through calculation of the actual failure condition and the theoretical life of the equipment and serve as the basis of a predictive maintenance model. Processing the acquired data includes: for the acceleration sensor, firstly, the noise of the acquired original signal is eliminated by utilizing Walsh transformation and the like, then envelope demodulation is carried out by utilizing Teager and the like, finally, the Walsh coefficient is filtered by utilizing a soft threshold value, and the Walsh inverse transformation is carried out, so that the data to be analyzed finally are obtained.
Module M4: and constructing a predictive maintenance model of equipment faults, and analyzing the processed data to obtain the health status condition and fault diagnosis result of the equipment. The predictive maintenance model is constructed based on the failure experience data of the equipment and the life prediction theory data of the equipment, and the operation condition and the health state of the equipment are judged through the change, trend and threshold value of the extracted physical characteristics of the failure mode of the equipment.
Module M5: and automatically forming a predictive maintenance mode and a plan according to the diagnosis result, and early warning a user to perform related maintenance work.
Those skilled in the art will appreciate that the invention provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (10)
1. A predictive maintenance method for a hydraulic oil source device, comprising:
step S1: analyzing failure modes of key components of hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components;
step S2: determining equipment characterization points of the important physical characteristics, and setting corresponding sensors at the equipment characterization points;
step S3: determining a transmission mode of data acquired by a corresponding sensor, and storing and processing the acquired data;
step S4: constructing a predictive maintenance model of equipment faults, and analyzing the processed data to obtain the health status condition and fault diagnosis result of the equipment;
step S5: and automatically forming a predictive maintenance mode and a predictive maintenance plan according to the diagnosis result, and early warning a user to perform related maintenance work.
2. The predictive maintenance method for a hydraulic oil source apparatus of claim 1, wherein the critical components include a motor-pump assembly, a source assembly, a hydraulic control element of the hydraulic oil source apparatus;
the failure mode is a fault that the equipment cannot normally operate due to various factors;
the important physical characteristics comprise temperature, vibration, rotating speed, pressure, flow, liquid level and oil cleanliness;
the sensor comprises a temperature sensor, an acceleration sensor, a rotation speed sensor, a pressure sensor, a flow sensor, a liquid level sensor and an online granularity sensor.
3. The predictive maintenance method for a hydraulic oil source apparatus according to claim 2, wherein the temperature sensor is provided at a tank end of the oil source assembly and an oil chamber of the motor-pump assembly for monitoring the oil temperature in the tank and the temperature of the decelerator oil in the pump unit;
the acceleration sensor is arranged at one end of the rigid body of the coupler of the motor-pump assembly, which is close to the rigid assembly of the pump assembly, and is used for monitoring the integral vibration condition of the motor-pump assembly;
the rotating speed sensor is arranged at the rotor end of the motor and used for monitoring the rotation condition of the motor;
the pressure sensor is arranged at the rear end of the hydraulic control element and used for monitoring the pressure pulsation condition of the system, and is also arranged at the two ends of the high-pressure filter of the equipment and used for monitoring the pollution condition and the whole abrasion condition of the oil filter;
the flow sensor is provided with an equipment external leakage pipeline for monitoring the leakage condition of the system;
the liquid level sensor is arranged in the oil tank of the oil source assembly and is used for monitoring the oil storage condition of the system;
the online granularity sensor is arranged at the rear end of the system high-pressure oil filter pipeline and is used for monitoring the cleanliness of the system oil.
4. The predictive maintenance method for a hydraulic oil source apparatus according to claim 2, wherein the data collected in step S3 includes on-line operation data, failure experience data, and life prediction theory data of the hydraulic oil source apparatus;
the online operation data comprise sensor data of the hydraulic oil source equipment and external sensor data with increased predictive maintenance, and the online operation data are collected to an upper computer or a server through a communication interface and a TCP/IP protocol and are used for analyzing real-time conditions of the equipment;
the failure experience data and the life prediction theoretical data are obtained through calculation of actual failure conditions and theoretical life of equipment and serve as the basis of a predictive maintenance model.
5. The predictive maintenance method for a hydraulic oil source apparatus according to claim 4, wherein the processing of the collected data in step S3 includes:
for the acceleration sensor, firstly, the noise of the acquired original signal is eliminated by utilizing Walsh transformation and the like, then envelope demodulation is carried out by utilizing Teager and the like, finally, the Walsh coefficient is filtered by utilizing a soft threshold value, and the Walsh inverse transformation is carried out, so that the data to be analyzed finally are obtained.
6. The predictive maintenance method for a hydraulic oil source device according to claim 1, wherein the predictive maintenance model is constructed based on failure experience data of the device and life prediction theory data of the device, and the operation condition and health state of the device are determined by the extracted change, trend and threshold of the physical characteristics of the failure mode of the device.
7. A predictive maintenance system for a hydraulic oil source apparatus, comprising:
module M1: analyzing failure modes of key components of hydraulic oil source equipment, and identifying important physical characteristics of failure of the key components;
module M2: determining equipment characterization points of the important physical characteristics, and setting corresponding sensors at the equipment characterization points;
module M3: determining a transmission mode of data acquired by a corresponding sensor, and storing and processing the acquired data;
module M4: constructing a predictive maintenance model of equipment faults, and analyzing the processed data to obtain the health status condition and fault diagnosis result of the equipment;
module M5: and automatically forming a predictive maintenance mode and a predictive maintenance plan according to the diagnosis result, and early warning a user to perform related maintenance work.
8. The predictive maintenance system for a hydraulic oil source apparatus of claim 7, wherein the critical components include a motor-pump assembly, a source assembly, a hydraulic control element of the hydraulic oil source apparatus;
the failure mode is a fault that the equipment cannot normally operate due to various factors;
the important physical characteristics comprise temperature, vibration, rotating speed, pressure, flow, liquid level and oil cleanliness;
the sensor comprises a temperature sensor, an acceleration sensor, a rotation speed sensor, a pressure sensor, a flow sensor, a liquid level sensor and an online granularity sensor.
9. The predictive maintenance system for a hydraulic oil source apparatus of claim 8, wherein the temperature sensor is disposed at a tank end of the oil source assembly and at an oil chamber of the motor-pump assembly for monitoring oil temperature in the tank and a speed reducer oil temperature condition in the pump stack;
the acceleration sensor is arranged at one end of the rigid body of the coupler of the motor-pump assembly, which is close to the rigid assembly of the pump assembly, and is used for monitoring the integral vibration condition of the motor-pump assembly;
the rotating speed sensor is arranged at the rotor end of the motor and used for monitoring the rotation condition of the motor;
the pressure sensor is arranged at the rear end of the hydraulic control element and used for monitoring the pressure pulsation condition of the system, and is also arranged at the two ends of the high-pressure filter of the equipment and used for monitoring the pollution condition and the whole abrasion condition of the oil filter;
the flow sensor is provided with an equipment external leakage pipeline for monitoring the leakage condition of the system;
the liquid level sensor is arranged in the oil tank of the oil source assembly and is used for monitoring the oil storage condition of the system;
the online granularity sensor is arranged at the rear end of the system high-pressure oil filter pipeline and is used for monitoring the cleanliness of the system oil.
10. The predictive maintenance system for a hydraulic oil source device of claim 8, wherein the data collected in module M3 includes on-line operational data, fault empirical data, and life prediction theory data for the hydraulic oil source device;
the online operation data comprise sensor data of the hydraulic oil source equipment and external sensor data with increased predictive maintenance, and the online operation data are collected to an upper computer or a server through a communication interface and a TCP/IP protocol and are used for analyzing real-time conditions of the equipment;
the failure experience data and the life prediction theoretical data are obtained through calculation of actual failure conditions and theoretical life of equipment and serve as the basis of a predictive maintenance model.
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| CN202311535929.XA CN117745256A (en) | 2023-11-16 | 2023-11-16 | Predictive maintenance method and system for hydraulic oil source equipment |
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| CN202311535929.XA CN117745256A (en) | 2023-11-16 | 2023-11-16 | Predictive maintenance method and system for hydraulic oil source equipment |
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