Fault diagnosis method, system and equipment for pumping unit motor and medium

Abstract

The invention discloses a fault diagnosis method, system and device for a pumping unit motor and a medium, and relates to the technical field of motor fault diagnosis, and the method comprises the steps: collecting the operation parameters of the pumping unit motor in an extremely cold environment; converting the operation parameters into a fault type through a fuzzy logic rule; using a genetic algorithm to optimize the neural network model, training the optimized neural network model to identify a fault type, and obtaining a fault diagnosis model; and inputting the detected operation parameters of the pumping unit motor into the fault diagnosis model to obtain a fault diagnosis result. According to the invention, the fault of the motor of the oil pumping unit can be accurately judged in an extremely cold environment.

Description

Technical Field

[0001] The present invention relates to the technical field of motor fault diagnosis, and in particular to a method, system, equipment and medium for fault diagnosis of an oil pump motor. Background Art

[0002] The operation of oil pump motors in extremely cold environments faces many challenges, such as drastic temperature changes, increased mechanical loads, and deteriorating lubrication conditions, which lead to frequent motor failures.

[0003] In the prior art, there are three methods for fault diagnosis of oil pump motors. Method 1 is based on threshold detection, which can trigger a fault alarm. Method 2 is based on vibration signal analysis, which determines mechanical faults by analyzing the spectral characteristics of vibration signals. It is often used to detect abnormalities of bearings, gears and other components. Method 3 is based on statistical analysis, which uses statistical methods to analyze the fluctuations and trends of operating parameters and detect abnormalities.

[0004] However, the operating parameters and fault characteristics of the oil pump motor in an extremely cold environment are uncertain and ambiguous, and the existing technology cannot accurately identify the faults of the oil pump motor in an extremely cold environment. Summary of the invention

[0005] The embodiments of the present invention provide a method, system, device and medium for diagnosing faults of an oil pump motor, which can solve the problem in the prior art that it is impossible to accurately identify faults of an oil pump motor in an extremely cold environment.

[0006] An embodiment of the present invention provides a fault diagnosis method for an oil pumping unit motor, comprising the following steps: collecting operating parameters of the oil pumping unit motor in an extremely cold environment; using the operating parameters as the rule basis of fuzzy logic rules, and determining the fault type according to the rules; using the operating parameters corresponding to the fault type to train a neural network model to identify the fault type, and using chromosomes in a genetic algorithm during the training process to optimize the parameters of the neural network model to obtain a fault diagnosis model; inputting the detected operating parameters of the oil pumping unit motor into the fault diagnosis model to obtain a fault diagnosis result.

[0007] Furthermore, the method of determining the fault type according to the rules comprises the following specific steps: defining a fuzzy set for each operating parameter; and establishing fuzzy logic rules for fault diagnosis; converting each operating parameter into the membership of the fuzzy set corresponding to each operating parameter through a membership function; and applying fuzzy logic rules to the fuzzy set according to the membership of the fuzzy set to defuzzify the fuzzy set into a fault type.

[0008] Furthermore, the parameters of the neural network model are optimized using chromosomes in the genetic algorithm during the training process, and the specific steps include: using the parameters of the neural network model as the chromosomes in the genetic algorithm, and randomly selecting multiple chromosomes from all chromosomes as the initial population; decoding each chromosome in the initial population into a parameter of the neural network algorithm, using the operating parameters of the pumping unit motor in an extremely cold environment to train the neural network algorithm to identify the fault type, evaluating the accuracy of the neural network algorithm through a fitness function, and using the accuracy as the fitness; selecting chromosomes with high fitness and randomly pairing them, performing a crossover operation on the chromosomes that have completed the random pairing to generate new chromosomes; randomly selecting some chromosomes from the new chromosomes for mutation, which can increase the population diversity; performing multiple iterative operations between selecting chromosomes with high fitness and the mutation step until a set number of iterations is reached or a set fitness threshold is reached; in the population composed of multiple chromosomes after the iterative operation is completed, selecting a chromosome with high fitness as the optimal solution, decoding the chromosome corresponding to the optimal solution into a parameter of the neural network, and completing the optimization of the neural network model.

[0009] Furthermore, the collecting of operating parameters of the oil pump motor in an extremely cold environment specifically comprises the following steps: collecting temperature changes, vibration conditions and current changes of the oil pump motor respectively through a temperature sensor, a vibration sensor and a current sensor.

[0010] An embodiment of the present invention provides a fault diagnosis system for an oil pumping unit motor, comprising: a data acquisition module, used to collect operating parameters of the oil pumping unit motor in an extremely cold environment; a model training module, used to use the operating parameters as the rule basis of fuzzy logic rules, and determine the fault type according to the rules; use the operating parameters corresponding to the fault type to train a neural network model to identify the fault type, and use chromosomes in a genetic algorithm to optimize the parameters of the neural network model during the training process to obtain a fault diagnosis model; a fault diagnosis module, used to input the detected operating parameters of the oil pumping unit motor into the fault diagnosis model to obtain a fault diagnosis result.

[0011] An embodiment of the present invention provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor implements the above-mentioned method for diagnosing faults of an oil pump motor when executing the computer program.

[0012] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for diagnosing faults of an oil pump motor is implemented.

[0013] The embodiments of the present invention provide a method, system, device and medium for diagnosing faults of an oil pumping unit motor. Compared with the prior art, the advantageous effects thereof are as follows:

[0014] The fuzzy logic rules convert the collected operating parameters of the oil pump motor in an extremely cold environment into fault types, and train a fault diagnosis model that can identify faults; the fault diagnosis model is used to directly convert the detected operating parameters into accurate fault types, so that the present invention can accurately identify faults of the oil pump motor in an extremely cold environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a system provided by an embodiment of the present invention;

[0016] Figure 2 An industrial flow chart of a fuzzy logic controller provided by an embodiment of the present invention;

[0017] Figure 3 A genetic algorithm optimization flow chart provided for an embodiment of the present invention;

[0018] Figure 4 This is a comparison chart of the effects provided by the embodiments of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0020] See also Figure 1 to Figure 4 The embodiment of the present invention provides a method for diagnosing a fault of an oil pumping unit motor, comprising the following steps:

[0021] Step 1: Collect the operating parameters of the oil pump motor in an extremely cold environment.

[0022] Step 2: Use the operating parameters as the rule basis of fuzzy logic rules and determine the fault type according to the rules; use the operating parameters corresponding to the fault type to train the neural network model to identify the fault type, and use the chromosomes in the genetic algorithm to optimize the parameters of the neural network model during the training process to obtain a fault diagnosis model.

[0023] Step 3: Input the detected operating parameters of the oil pump motor into the fault diagnosis model to obtain the fault diagnosis results.

[0024] The present invention proposes a motor fault diagnosis system for an extremely cold environment oil pump based on a fuzzy intelligent algorithm. The system collects motor operation data in real time through a sensor module, pre-processes the data using a data acquisition module, and analyzes and diagnoses the pre-processed data through a fuzzy logic controller and an intelligent algorithm module, thereby realizing rapid and accurate detection and classification of motor faults. This method not only improves the accuracy and response speed of fault diagnosis, but also enhances the adaptability and stability of the system, effectively reduces maintenance costs, and has broad application prospects.

[0025] The specific system modules are as follows:

[0026] 1. Sensor module: used to collect the operating parameters of the oil pump motor in an extremely cold environment, such as temperature, vibration and current.

[0027] 2. Data acquisition module: connected to the sensor module, used to collect and pre-process sensor data, including data filtering and normalization processing.

[0028] 3. Fuzzy logic controller: connected to the data acquisition module, used to apply fuzzy logic rules to analyze and process the pre-processed data, including fuzzification unit, fuzzy rule base and defuzzification unit.

[0029] 4. Intelligent algorithm module: connected with the fuzzy logic controller, used to combine neural network or genetic algorithm to optimize the rules and parameters of the fuzzy logic controller to improve the accuracy and efficiency of fault diagnosis.

[0030] 5. Fault diagnosis module: connected to the intelligent algorithm module, used for real-time monitoring and fault diagnosis of the motor's operating status based on the optimized fuzzy logic model, including a fault detection unit and a fault classification unit.

[0031] The system of the present invention also includes a remote monitoring module for transmitting the fault diagnosis result to a remote monitoring center to facilitate remote fault management and maintenance.

[0032] The advantages of the present invention are:

[0033] 1. Improve diagnostic accuracy: By combining fuzzy logic with intelligent algorithms, the fault characteristics of the motor can be captured and analyzed more accurately, improving the accuracy of fault diagnosis.

[0034] 2. Quick response: The system can monitor the operating status of the motor in real time and quickly diagnose and respond when a fault occurs, reducing equipment damage and downtime.

[0035] 3. Enhanced adaptability: Fuzzy logic and intelligent algorithms can handle complex and changeable working conditions in extremely cold environments, improving the adaptability and stability of the system.

[0036] 4. Reduce maintenance costs: By improving the accuracy and response speed of fault diagnosis, unnecessary maintenance work and equipment downtime are reduced, thereby reducing maintenance costs.

[0037] The present invention provides an efficient and reliable extreme cold environment oil pump motor fault diagnosis system, which has broad application prospects and can significantly improve the operating reliability and production efficiency of the oil pump motor in extreme environments.

[0038] The embodiment of the present invention provides a fault diagnosis system for an oil pump motor, comprising: a data acquisition module, used to collect the operating parameters of the oil pump motor in an extremely cold environment. A model training module, used to use the operating parameters as the rule basis of the fuzzy logic rules, and determine the fault type according to the rules; use the operating parameters corresponding to the fault type to train a neural network model to identify the fault type, and use the chromosomes in the genetic algorithm to optimize the parameters of the neural network model during the training process to obtain a fault diagnosis model. A fault diagnosis module, used to input the detected operating parameters of the oil pump motor into the fault diagnosis model to obtain a fault diagnosis result.

[0039] An embodiment of the present invention provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor implements the steps of a fault diagnosis method for an oil pump motor when executing the computer program.

[0040] An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of a method for diagnosing a fault of an oil pump motor are implemented.

[0041] The specific embodiments are as follows:

[0042] Embodiment 1:

[0043] The following introduces a specific system structure and workflow:

[0044] 1. System structure.

[0045] The extremely cold environment pumping unit motor fault diagnosis system based on fuzzy intelligent algorithm of the present invention comprises the following modules:

[0046] 1. Sensor module: Temperature sensor: used to detect temperature changes during motor operation. Vibration sensor: used to monitor the vibration of the motor. Current sensor: used to measure current changes during motor operation.

[0047] 2. Data acquisition module: Data filter: used to filter the sensor data and remove noise. Data normalization unit: normalizes the filtered data for subsequent processing.

[0048] 3. Fuzzy logic controller: Fuzzification unit: converts normalized data into fuzzy sets. Fuzzy rule base: stores fuzzy logic rules for fault diagnosis. Defuzzification unit: converts the diagnosis results of fuzzy sets into clear fault types.

[0049] 4. Intelligent algorithm module: Neural network optimization unit: train and optimize fuzzy logic rules through neural network algorithm. Genetic algorithm optimization unit: use genetic algorithm to perform evolutionary optimization on fuzzy logic rules and parameters.

[0050] 5. Fault diagnosis module: Fault detection unit: monitors the motor running status in real time to detect whether there is a fault. Fault classification unit: classifies and diagnoses the fault type according to the optimized fuzzy logic model.

[0051] 6. Remote monitoring module (optional): transmits diagnostic results to the remote monitoring center to facilitate remote fault management and maintenance.

[0052] 2. Workflow.

[0053] 1. Data acquisition: The sensor module collects the motor’s operating data in real time, including temperature, vibration and current data, and transmits it to the data acquisition module.

[0054] 2. Data preprocessing: The data acquisition module filters and normalizes the sensor data to remove noise and standardize the data.

[0055] 3. Fuzzy processing: The fuzzification unit of the fuzzy logic controller converts the preprocessed data into fuzzy sets.

[0056] 4. Rule application: The fuzzy rule base analyzes fuzzy sets according to predefined fuzzy logic rules.

[0057] 5. Optimization processing: The intelligent algorithm module uses neural networks and genetic algorithms to optimize the rules and parameters of the fuzzy logic controller.

[0058] 6. Fault diagnosis: The defuzzification unit converts the optimized fuzzy diagnosis results into clear fault types, and the fault diagnosis module performs real-time monitoring and classification diagnosis.

[0059] 7. Remote monitoring (optional): Fault diagnosis results are transmitted to the remote monitoring center through the remote monitoring module for remote fault management and maintenance.

[0060] Embodiment 2:

[0061] The following is a specific application example:

[0062] 1. Experimental environment.

[0063] A pumping motor operating in an extremely cold environment was selected as the experimental object, and the experimental environment temperature was between -40℃ and -20℃.

[0064] 2. Experimental steps.

[0065] 1. Sensor layout: Install temperature sensors, vibration sensors and current sensors at key parts of the motor to ensure the comprehensiveness and accuracy of data collection.

[0066] 2. Data acquisition and preprocessing: Start the motor, the sensor module collects the motor operation data in real time, and the data acquisition module filters and normalizes the data.

[0067] 3. Fuzzy logic analysis: The preprocessed data is input into the fuzzy logic controller, converted into fuzzy sets through the fuzzification unit, and analyzed using the fuzzy rule base.

[0068] 4. Intelligent algorithm optimization: The intelligent algorithm module optimizes the rules and parameters of the fuzzy logic controller using neural networks and genetic algorithms.

[0069] 5. Fault diagnosis and classification: The fault diagnosis module monitors the motor operating status in real time and classifies and diagnoses the detected faults.

[0070] 6. Result output: The diagnostic results are transmitted to the remote monitoring center through the remote monitoring module, and a detailed fault report is generated.

[0071] 3. Result analysis.

[0072] Through experimental verification, the system of the present invention can realize real-time fault diagnosis of oil pump motors in extremely cold environments, the diagnostic accuracy and response speed are significantly improved, the adaptability and stability of the system are enhanced, and the experimental results show that the system has good effects in practical applications.

[0073] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for diagnosing faults of an oil pumping unit motor, characterized in that: The following steps are involved: Collect the operating parameters of the pumping unit motor in an extremely cold environment; Taking the operating parameters as the rule basis of fuzzy logic rules, the fault type is determined according to the rules; The operation parameters corresponding to the fault type are used to train the neural network model to identify the fault type, and the chromosomes in the genetic algorithm are used to optimize the parameters of the neural network model during the training process to obtain a fault diagnosis model; The detected operating parameters of the pumping unit motor are input into the fault diagnosis model to obtain the fault diagnosis results.

2. A method for diagnosing a fault of an oil pumping unit motor as claimed in claim 1, characterized in that: The specific steps of determining the fault type according to the rule include: Define fuzzy sets for each operating parameter; and establish fuzzy logic rules for fault diagnosis; Each operating parameter is converted into the membership of the fuzzy set corresponding to each operating parameter through a membership function; According to the membership degree of the fuzzy set, the fuzzy set is defuzzified into fault types using fuzzy logic rules.

3. A method for diagnosing a fault of an oil pumping unit motor as claimed in claim 1, characterized in that: The chromosomes in the genetic algorithm are used to optimize the parameters of the neural network model during the training process, and the specific steps include: The parameters of the neural network model are used as chromosomes in the genetic algorithm, and multiple chromosomes are randomly selected from all chromosomes as the initial population; Decode each chromosome in the initial population into the parameters of the neural network algorithm, use the operating parameters of the pumping unit motor in an extremely cold environment to train the neural network algorithm to identify the fault type, evaluate the accuracy of the neural network algorithm through the fitness function, and use the accuracy as the fitness; Select chromosomes with high fitness and pair them randomly, perform crossover operations on the chromosomes that have completed random pairing, and generate new chromosomes; Randomly selecting some chromosomes in the new chromosomes for mutation can increase population diversity; Perform multiple iterations between selecting a chromosome with high fitness and the mutation step until the set number of iterations or the set fitness threshold is reached; In the population composed of multiple chromosomes after the iterative operation is completed, the chromosome with high fitness is selected as the optimal solution, and the chromosome corresponding to the optimal solution is decoded into the parameters of the neural network to complete the optimization of the neural network model.

4. A method for diagnosing a fault of an oil pumping unit motor as claimed in claim 1, characterized in that: The specific steps of collecting the operating parameters of the oil pumping unit motor in an extremely cold environment include: The temperature change, vibration condition and current change of the oil pump motor are respectively collected through the temperature sensor, vibration sensor and current sensor.

5. A fault diagnosis system for an oil pump motor, characterized in that: include: Data acquisition module, used to collect the operating parameters of the pumping unit motor in an extremely cold environment; The model training module is used to use the operating parameters as the rule basis of the fuzzy logic rules and determine the fault type according to the rules; use the operating parameters corresponding to the fault type to train the neural network model to identify the fault type, and use the chromosomes in the genetic algorithm to optimize the parameters of the neural network model during the training process to obtain a fault diagnosis model; The fault diagnosis module is used to input the detected operating parameters of the pumping unit motor into the fault diagnosis model to obtain the fault diagnosis result.

6. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements a method for diagnosing a fault of an oil pump motor according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a fault diagnosis method for an oil pump motor according to any one of claims 1 to 4 is implemented.

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