A brief review of condition monitoring techniques for the induction motor

Soother, Dileep Kumar; Daudpoto, Jawaid

doi:10.1139/tcsme-2018-0234

Cited by 18 publications

(13 citation statements)

References 37 publications

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“…In the case of regression-based models, these techniques help lower the order of function to be mapped by DL models, which result in computational efficiency and robust real-time deployment. Popular feature selection techniques are principal component analysis (PCA), linear discriminant analysis (LDA), and independent component analysis (ICA) [41,46]. ese techniques are particularly known for their ability to reduce the dimensionality of the dataset.…”

Section: Feature Processing For Dl-based Condition Monitoring Of Motorsmentioning

confidence: 99%

“…ey found that noninvasive techniques such as thermal imaging are overcoming the conventional condition monitoring methods. On the other hand, in [41] authors have briefly reviewed various condition monitoring techniques particularly based on current, vibration, and acoustic signals. ere is therefore still a need for a comprehensive review on the applications of DL models in condition monitoring of motors.…”

Section: Introductionmentioning

confidence: 99%

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The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

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Section: Feature Processing For Dl-based Condition Monitoring Of Motorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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“…Recently, AI techniques have become increasingly applied in vehicles fault diagnosis due to the significant impact [8]. In this regard, the authors in [9] proposed a self-diagnosis system for autonomous vehicles that aims to improve the self-diagnosis speed and reduce the overhead.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Towards Intelligent Vehicle Fault Diagnosis

Al-Zeyadi

Andreu-Pérez

Hagras

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT.Index Terms-AI, deep learning, deep neural network, vehicle fault diagnosis, Internet of Things (IoT).

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“…Thus, early fault detection in rotating machines is a goal that ultimately assists in saving system from major damages Generally, a motor requires electric current for rotor to produce torque, which is obtained by electromagnetic induction from magnetic field of stator winding. In various industries 3phase IMs are widely used because these motors are efficient, reliable, compact, less expensive, and easy to operate [6,9]. Three phase motors are also used in electric vehicles and railways owing to its robust structure and high performance [10].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learninag based Non-invasive and Real-Time Fault Detection System for 3-Phase Induction Motors

Fahim

Soother²,

Harijan³

et al. 2021

SJET

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Induction motor plays a major role in industry. Despite of its strong structure, induction motors are often prone to faults. There are different types of faults that occurs in the induction motor such as bearing faults, winding faults, etc. Thus motors in major applications require continuous and effective monitoring. In this paper, a stand-alone and non-invasive condition monitoring system that can monitor the condition of 3-phase induction motor using motor current signatures with aid of deep learning (DL) approaches. The proposed system extracts the features using non-invasive current sensors it further samples the features using an analog to digital converter (ADC) and organizes the data acquired from ADC using Raspberry-pi microcomputer. The current data acquired from induction motor is used to train and test the DL models including Multilayer Perceptron (MLP), Long Short-term Memory (LSTM), and One-Dimensional Convolutional Neural Networks (1DCNN). The comparative analysis is demonstrated and the LSTM model as best fault classifier among all with accuracy up to 100%. Finally, the real-time testing of the device showed that the developed system can effectively monitor the conditions of motor using non-invasive current sensors.

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A brief review of condition monitoring techniques for the induction motor

Cited by 18 publications

References 37 publications

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Deep Learning Towards Intelligent Vehicle Fault Diagnosis

A Deep Learninag based Non-invasive and Real-Time Fault Detection System for 3-Phase Induction Motors

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