Motor Fault Diagnosis System Research Articles

An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM

An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM

Using a One-Dimensional Convolutional Neural Network with Taguchi Parametric Optimization for a Permanent-Magnet Synchronous Motor Fault-Diagnosis System

Hyperparameter tuning requires trial and error, which is time consuming. This study employed a one-dimensional convolutional neural network (1D CNN) and Design of Experiments (DOE) using the Taguchi method for optimal parameter selection, in order to improve the accuracy of a fault-diagnosis system for a permanent-magnet synchronous motor (PMSM). An orthogonal array was used for the DOE. One control factor with two levels and six control factors with three levels were proposed as the parameter architecture of the 1D CNN. The identification accuracy and loss function were set to evaluate the fault-diagnosis system in the optimization design. Analysis of variance (ANOVA) was conducted to design multi-objective optimization and resolve conflicts. Motor fault signals measured by a vibration spectrum analyzer were used for fault diagnosis. The results show that the identification accuracy of the proposed optimization method reached 99.91%, which is higher than the identification accuracy of 96.75% of the original design parameters before optimization. With the proposed method, the parameters can be optimized with a good DOE and the minimum number of experiments. Besides reducing time and the use of resources, the proposed method can speed up the construction of a motor fault-diagnosis system with excellent recognition.

Fault Diagnosis and Tolerant Control for Three-Level T-Type Inverters

This paper proposes a fault diagnosis system for inverters based on a cerebellar model articulation controller (CMAC). First, a three-level T-type inverter was implemented and used to create a three-level T-type inverter test environment for measuring the output voltage waveforms of faulty power transistors on the main inverter circuit under different output frequencies. The measured waveforms were processed using a fast Fourier transform (FFT) algorithm to create frequency spectrum diagrams and extract the characteristic spectra of corresponding faulty switches. Then, the associations of the spectra were determined and applied as training data for the CMAC to detect the positions of the faulty power transistors. The test results demonstrated that the proposed induction motor fault diagnosis system is capable of fast algorithm, requires less data to train, and has excellent accuracy of identification, with an error margin of ±5%. The detection results were then processed using a fault-tolerant controller (FTC) to enhance the reliability of the proposed system. Finally, some simulations and experimental results were conducted and analyzed to validate the feasibility of the proposed FTC system.

딥 러닝에 의한 전동기 기계적 고장 수준 결정을 위한 전동기 진동/전류 데이터 특성 분석 연구

In the classic motor fault diagnosis system, a method of analyzing the differences between the normal and collected state signals of the motor to be diagnosed was used and the method can diagnose only the limited situations because the diagnosis is based on the frequency of the mechanical failure. In order to compensate for this, some studies on a system that performs more specialized fault diagnosis through deep learning algorithms were carried out. However, the level of failure cannot be determined because these studies consider only the signals that have a great influence on motor operation. To solve this problem, the characteristics of vibration and current signals are analyzed to develop a deep learning algorithm suitable for fault level determination. The characterized signals are used for fault diagnosis and prediction. Fault diagnosis based on vibration signal is carried out through DT-CNN (Decision Tree Convolutional Neural Network). In addition, it is checked whether the current signal in the initial failure state, which is relatively insensitive to failure, can be classified through a deep learning algorithm. The proposed data utilization performance was evaluated through an induction motor and the analyzed signal-based fault diagnosis system is expected to enable a more precise diagnosis compared to the existing system.

Establish Induction Motor Fault Diagnosis System Based on Feature Selection Approaches with MRA

This paper proposes a feature selection (FS) approach, namely, correlation and fitness value-based feature selection (CFFS). CFFS is an improvement feature selection approach of correlation-based feature selection (CFS) for the common failure cases of the induction motor. CFFS establishes the induction motor fault detection (FD) system with artificial neural network (ANN). This study analyzes the current signal of the induction motor with multiresolution analysis (MRA), extracts the features, and uses feature selection approaches (ReliefF, CFS, and CFFS) to reduce the number of features and maintain the accuracy of the induction motor fault detection system. Finally, the induction motor fault detection system is trained by the feature selection approaches selected features. The best induction motor fault detection system will be established through the comparison of the efficiency of these FS approaches.

Fault Diagnosis of Induction Motor Using Convolutional Neural Network

Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.

A Motor Fault Diagnosis System Based on Cerebellar Model Articulation Controller

The purpose of this paper is to construct a smart induction motor fault diagnosis system with cerebellar model articulation controller (CMAC). First, we divide induction motor faults in three kinds, rotor mandrel fault, bearing fault and electrical fault. Then, we subdivide them into ten types, in each of which the vibration signal spectra of induction motors were measured and sorted for establishing the individual fault types. From the information on motor faults, we identify representative characteristic frequency spectra for the faults to further establish the correlations between each of the fault types and its corresponding characteristic frequency spectrum as the basis for the development of a motor fault diagnosis system. In this paper, the theoretic basis is CMAC, and the data of vibration signal spectrum measured against the motor faults are used to train the fault diagnosis system. We then conducted fault diagnoses with the data of actual motor run. The test results demonstrated that the proposed induction motor fault diagnosis system is capable of fast algorithm, requires less data to train with, as well as has excellent power of identification.

Fault analysis and diagnosis system for induction motors

This study analyzes the vibration signals of fault induction motors for establishing an intelligent motor fault diagnosis system by using an extension neural network (ENN). Extension theory and a neural network (NN) are combined to construct the motor fault diagnosis system, which identifies the most likely fault types in motors. First, the vibration signal spectra of the 10 most common fault types are measured and organized into individual motor fault models. Subsequently, according to the motor fault data, representative characteristic frequency spectra are identified, and the correlation between the motor fault types and their corresponding characteristic frequency spectra are established to develop the motor fault diagnosis system. Finally, the test results confirm that the proposed motor fault diagnosis system is fast, requires less training data, and demonstrates first-rate identification capability.

An industrial Fault Diagnosis System based on Bayesian Networks

This paper presents a DC motor fault diagnosis system based on Bayesian networks. This was done by the design of a new electromechanical test bed allowing the collection of functioning data from a real world industrial Direct current (DC) Motor. The data collection will help in the construction of Bayesian networks models. These data are collected from sensors measuring different types of variables that are directly related to the industrial system. Without doing any mathematical modeling that describes the physical properties of the studied DC motor, the proposed tool provides with the help of Bayesian networks parameters and structure learning algorithms, the base to construct a fault diagnosis tool that can be extended to a fault prognosis tool.

The Fault Recognition of Motor Based on the Fusion of Neural Network and D-S Evidence Theory

In order to reflect the motor from various aspects and realize the motor system state failure mode automatic identification and accurate diagnosis, neural network combined with the D-S evidence theory to form the motor fault diagnosis system. In data fusion level, fault characteristic is classified; and then the fault feature is extracted by the BP neural network and the local fault of the motor is diagnosed, as a result, the independent evidence is obtained; at last the D-S evidence theory fusion algorithm is used on the evidence to achieve the fault of the motor accurate diagnosis.Broken test proved that the diagnosis system improves the motor of the fault diagnosis of accuracy, and can meet the needs of real-time diagnosis. The diagnostic test proved that the diagnosis system improves the accuracy of motor fault diagnosis, and can satisfy the diagnosis in real-time.