Induction Motor Fault Diagnosis Research Articles

An Adversarial Multisource Data Subdomain Adaptation Model: A Promising Tool for Fault Diagnosis of Induction Motor Under Cross-Operating Conditions

Typical domain adaptation neural network that takes multi-source heterogeneous data as input usually achieves poor diagnostic accuracy in induction motor fault diagnosis under cross-operating conditions. Aiming at this problem, the present study proposes an adversarial multi-source data subdomain adaptation (AMDSA) model. This model encapsulates three types of modules: a shared feature extractor, a label predictor and a series of domain discriminators. The joint operation of the shared feature extractor and the domain discriminators is used to perform subdomain adaptation of different types of data for obtaining domain invariant features of multi-source heterogeneous data. The label predictor is employed to fuse these domain invariant features and realize label classification. The proposed model can solve the problem of multi domain adaptation in multi-source heterogeneous data through constructing a subdomain adaptation strategy and a feature fusion strategy. The effectiveness of AMDSA is verified by a series of diagnostic experiments on faulty induction motors under cross-operating conditions. The experimental results show that the average diagnostic accuracy of all cross-operating conditions reaches 97.62%.

Experimental Investigation of the Combined Fault: Mechanical and Electrical Unbalances in Induction Motors Based on Stator Currents Monitoring

Numerous studies examine the electrical and mechanical, internal and exterior problems of induction motors separately. However, this type of machines is also susceptible to several combined faults which affect it at the same time. The aim of this work is to study firstly the effects of the mass imbalance with the supply voltage unbalance faults in a combined state, and specifically its effects on the stator currents (distortion of currents waveform and augmentation of current unbalance factor (CUF)). The findings of this research were derived from experimental tests, which enabled us to produce the combined voltage and mass imbalance fault intentionally. The second purpose of this paper is to identify these irregularities. In the field of induction motor fault diagnosis, the stator current analysis techniques have shown to be quite successful and have gained widespread use. Therefore, we used the well-known method motor current signature analysis (MCSA). In addition, we have strengthened the research by using spectral analysis on the Park's components (Id and Iq). The obtained results demonstrate the efficacy of analyzing stator currents and Park components spectra (particularly the direct Park component (Id)) for detecting this kind of electrical and mechanical combined defects

Fault Diagnosis of Induction Motor via Combined Sliding Mode and Disturbance Observers

Fault Diagnosis of Induction Motor via Combined Sliding Mode and Disturbance Observers

Sensor fault diagnosis technique applied to three-phase induction motor drive

This paper conducts research on sensor fault diagnosis for a three-phase induction motor drive (IMD) in steady-state operation. An improving diagnostic technique based on the integration algorithm of the sinusoidal current signal is proposed for detecting and locating faulty current sensors in the induction motor drive. The IMD integrated a proposed fault detection-isolation (FDI) system is investigated for operating characteristics when sensor failures occur. The faulty sensor needs to be accurately identified and quickly isolated from the control system. Then the estimated signal will be used to replace the fault signal to retain the IMD stability. MATLAB/Simulink software will be applied to simulate the speed-torque characteristics of the IMD system as well as sensor failures occurring during operation. The performance of the proposed method will be evaluated through the accuracy and timeliness in each fault case corresponding to each sensor.

A Light Weight Multisensory Fusion Model for Induction Motor Fault Diagnosis

Fault diagnosis keeps an essential tool to ensure the safety and reliability of a motor system. Based on deep learning, fault diagnosis models constructed by mining historical fault data of equipment have received extensive attention. However, the high computational cost constrains the application of deep learning models for fault diagnosis, especially when coping with multisource data. Inspired by the model reduction and neural network structure automatic search method, this article proposed a light weight multisensory fusion model for induction motor data fusion and diagnosis. Inverted residual block and network architecture search technology are introduced to accelerate the training speed and prediction speed of the diagnostic model, so that the diagnostic accuracy is maintained at an acceptable level. The effectiveness of the proposed model is demonstrated through motor fault diagnosis experiments. Compared with other popular neural networks, the proposed method is capable of judging fault patterns accurately with shorter prediction time.

A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.

Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations

Induction motors are the horsepower in the industrial environment, and among them, 3-phase induction motors (3PIMs) stand out for their robustness and standard 3-phase power supply. In the literature, there are many approaches to diagnose faults for the nonlinear 3PIM model, and the vast majority focus on a single motor fault, although others address more faults but at the cost of greater computational complexity. In this sense, one of the methods with less computational load and early detection is the parity equation approach, which is based on analyzing the discrepancy between the input and output signals of a real process and a linear mathematical model to generate a residual signal, which contains important information about the fault and is obtained through a suitable selection of a weighting matrix W to isolate the faults as much as possible. The problem in this case study is that the 3PIM model is a nonlinear system. In this work, the fault detection method based on the parity equations approach applied in the 3PIM is explored using a simplified and proposed model of the 3PIM working in the D-Q synchronous reference frame, which is matched with the direct current motor model to guarantee both the existence of the parity space and to ensure a large set of detectable faults in the 3PIM parameters. Simulation and experimental results validate the proposed scheme and confirm a very simple set of residual equations to guarantee both early detection and a large set of detectable faults in: Stator and rotor resistances, stator and rotor inductances, as well as current, voltage, and speed sensors. Additionally, development of human machine interface (HMI) is implemented to validate the proposed scheme.

Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor

The objective of this paper is to apply deep learning network running on an embedded system platform to diagnose faults of a three-phase electric motor by a non-contact method based on operating motor noise. To accomplish this, at first, deep learning network should be designed and trained on a computer, and then converted to an equivalent network to run on the embedded system. The network input data is a two-dimension spectrogram image of the noise emitted by the motor in four main cases, including normal operation, phase shift, phase loss and bearing failure. The execution time and accuracy of these deep learning network structures will be deployed on three microcontrollers including ESP32, ESP32-C3 and nRF52840 to determine the suitable embedded platform and network structure for real-time running. Experimental results show that the proposed deep learning network models could diagnose the faults well on both computer and embedded platform with the highest accuracies are 99,7% and 99,3%, respectively. In particular, the preliminary results are remarkable with the recognition time and accuracy at 1,7 seconds and 72%, respectively associated with the proposed deep learning network on realtime embedded system performance.

Condition Monitoring and Fault Diagnosis of Induction Motor using DWT and ANN

Condition Monitoring and Fault Diagnosis of Induction Motor using DWT and ANN

Fault Diagnosis of Induction Motors Under Untrained Loads With a Feature Adaptation and Improved Broad Learning Framework

A variety of machine learning methods have good performance in fault diagnosis of induction motors under trained electric load. However, existing methods have low accuracy when diagnosing faults under untrained electric loads. In fact, it is impossible to train a system by using infinite number of electric loads. To solve this problem, a novel fault diagnosis framework including a training framework and an adaptation framework is proposed in this article. The system only needs to be trained by the training framework and rated load data, then, it can diagnose any other untrained loads by the adaptation framework. In the training framework, most training methods cannot automatically change their network structures to achieve a global maximum accuracy without overfitting. To address this issue, a broad learning (BL) with a particle swarm optimization is proposed. In the adaptation framework, most features (statistical feature and sample entropy) from untrained load are different from trained load. This degrades the diagnostic accuracy. To overcome this problem, an adaptive factor for statistical feature is, therefore, proposed to process the winding current data from untrained loads to be close to the data of trained load. At the same time, adaptive coefficient is proposed to adjust the sample entropy (SampEn) obtained from acoustic signal to ensure that the values of SampEn between untrained loads and trained load are similar. Even though features from untrained loads can be adjusted by the adaptation framework, the activation functions of BL trained by rated load are still different from those for untrained loads. To solve this issue, an improved scale exponential linear unit-broad learning with scale coefficient is, therefore, proposed to adapt the differences of the activation functions between the trained and untrained loads for enhancing the classification accuracy. Experimental results show that the proposed fault diagnostic framework is accurate under untrained loads.

Early Stator Fault Detection and Condition Identification in Induction Motor Using Novel Deep Network

Fault diagnosis of induction motor is an important task for both the researchers and industry. The interturn fault is one of the frequented faults and accounts for more than 37% of the failures. Thus, the diagnosis of interturn fault at an early stage has become vital to avoid the catastrophic failures and production losses. In this work, the advantages of deep learning-based methods are explored to detect the interturn fault at an incipient stage. Hybrid architectures has been proposed in this work for incipient inter turn fault diagnosis [i.e., 1D convolution neural network-long short-term memory (1DCNN-LSTM) and 1DCNN-gated recurrent unit (GRU) based methods]. The performance of the hybrid methods has been compared with standalone techniques (1DCNN, LSTM, and GRU), and the results show that the hybrid models (1DCNN-LSTM and 1DCNN-GRU) outperform the individual architectures (in terms of accuracy, sensitivity, and specificity), for fault diagnosis and isolation. The computational time has also been found to be comparable, which is an added advantage for fast diagnosis tasks. The approach combines the feature extraction and classification tasks, to perform early diagnosis, in presence of ambiguous conditions. Here, the incipient diagnosis of fault has been augmented with condition identification i.e., healthy (balanced voltages), healthy (unbalanced voltages), faulty (balanced voltages), and faulty (unbalanced voltages), to effect both early detection and isolation. The performance metrics show the robustness of the proposed method for not only detecting fault, but also identifying such confusing conditions for critical applications, in which induction motors are employed.

Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis.

The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.

An Infrared Thermal Image Few-Shot Learning Method Based on CAPNet and Its Application to Induction Motor Fault Diagnosis

The induction motor is widely used for providing the running power of rotating machinery. Its fault diagnosis is significant to ensure the operation safety of rotary machinery. Infrared thermal image analysis based on deep learning has attracted the attention of many researchers due to its advantages in non-destructive and space locations. However, obtaining sufficient high-quality thermal image samples in practical applications is relatively difficult. Developing few-shot learning models is significant for extending the engineering application of thermal image analysis. The existing models of few-shot learning often neglect the spatial information extraction of images at the feature extraction module, which limits the performance of models. In this paper, a new prototypical network with coordinate attention (CAPNet) for fault diagnosis of three-phase induction motor is proposed. The coordination attention feature extraction module (CAFEM) is designed by the coordinate attention block and the convolution blocks to obtain the spatial relativities between feature maps. The designed CAFEM can inhibit harsh requirements on the amount of data, which greatly enhances the CAPNet’s ability to mine information. Moreover, the features exacted of fault are identified by the metric classifier. The performance of the CAPNet method is validated by experimental analysis of thermal images of a three-phase induction motor. The superiority of the proposed method is demonstrated in comparison with six other state-of-the-art algorithms in few-shot learning.

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

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.

Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor

This work proposes a novel online detection scheme to diagnose incipient inter-turn short circuit fault and estimate the failure severity in induction motor. Incipient detection of the stator failure during the machine running, as well as identification of its intensity can reduce the risk of additional damage to the phase winding, improve the operational efficiency, and ensure machine availability. Hence, the incipient fault diagnosis provides a safe operating area for the motor. This work aims to specify the percentage of defective turns in the shorted winding by proposing a new mathematical parameter based on wavelet analysis, in addition to employ a multi-class support vector machine to perform the classification task. Discrete Wavelet Transform is used to analyze the stator currents after modeling the motor utilizing Clarke-Concordia transformation. From the detailed coefficients, Max and L2 norms are calculated. The adopted parameter is computed depending on the previous norms, which form the input vector to feed the classifier. The multi-class support vector machine–based one versus one algorithm is used to determine accurately the defect intensity. The acquired outcomes prove that the proposed approach, depending on the novel parameter along with multi-class support vector machine can give a robust and accurate indication about the machine status, which enables the estimation of the fault severity. To verify the competency of the methodology, various hardware experiments are carried out on the motor. The experimental results demonstrate the validity and practicability of the method, with a higher level of correctness, exceeding 96%.

Optical Fiber Sensor with Stable Operating Point for AC Magnetic Field Measurement

A novel alternating current (AC) magnetic field sensor that has a stable operating point and is insensitive to ambient temperature fluctuations is presented. The sensor is based on a high attenuation fiber Bragg grating (HAFBG) attached to a magnetostrictive rod. A stable operating point is achieved by regulating a heating laser based on a feedback algorithm that compensates the temperature fluctuations of the surrounding environment. Experimental results show that the sensor responds well to dynamic magnetic fields and is able to ensure a stable operating point in the range of at least 15 °C in an ambient temperature disturbance test. The ease of fabrication and excellent performance suggest that the proposed fiber sensor is suitable for practical AC magnetic field sensing applications, such as health monitoring of transformers and fault diagnosis of induction motors.

Graph Cardinality Preserved Attention Network for Fault Diagnosis of Induction Motor Under Varying Speed and Load Condition

During the long-term operation of motors, their working conditions are changing due to the industrial demands or declining health status, and traditional diagnosis methods perform poorly in that case. This article proposes a fault diagnosis method based on graph cardinality preserved attention network (GCPAT), which can work under varying working conditions, and can be generalized to the transient state. Diagnosis results are obtained by analyzing signal-converting graphs, which are composed of nodes and edges. First, the vibration signals are converted into symmetrical snowflake images by symmetrized dot pattern (SDP) method. Second, SLIC is developed to make homogeneous super-pixels in SDP images as nodes, and form graphs according to color, texture, and distance features. Finally, the GCPAT is utilized to distinguish motor status. Compared with other state-of-art methods, the results show the out-performance of GCPAT under varying working conditions both in steady and transient state.

Induction Motor Broken Rotor Bar Fault Diagnosis Based on Third-Order Energy Operator Demodulated Current Signal

Motor current signature analysis (MCSA) has been widely used for fault diagnosis of induction motors (IMs) because of its non-invasiveness and comparatively low cost. However, the spectral leakage of the fundamental supply frequency can obscure the fault characteristic component when IMs operating at no-load or low-load conditions. In this paper, a broken rotor bar (BRB) fault diagnosis approach based on the third-order energy operator (TOEO) demodulated current signals is presented. The TOEO approach performs effectively in reducing the negative impact of the fundamental supply frequency spectral leakage and enhancing the BRB fault features. Theoretical and experimental results show that the TOEO approach can excellently reveal the fault characteristic frequency, instead of the sideband frequencies. Besides, the results are compared with the Teager-Kaiser energy operator (TKEO). The capability of the presented approach has several advantages over the TKEO, especially when the IMs are fed by an inverter.

Comprehensive and Simplified Fault Diagnosis for Three-Phase Induction Motor Using Parity Equation Approach in Stator Current Reference Frame

In this paper, a complementary and simplified scheme to diagnose electrical faults in a three-phase induction motor using the parity equations approach during steady state operation bases on the stator current reference frame is presented. The proposed scheme allows us to identify the motor phase affected due to faults related to the stator side, such as current sensors, voltage sensors, and resistance. The results obtained in this work complement a detection system that uses the DQ model of the three-phase induction motor and parity equations focused on the synchronous reference frame, which can detect stator-side faults but cannot locate the affected phase. In addition, considering practical and operational aspects, the residual detection set obtained is simplified to three simple algebraic equations that are easy to implement. The simulation results using the PSIM simulation software and the experimental test allow us to validate the proposed scheme.