Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network

Modeling and Simulation in Engineering - Selected Problems ◽

10.5772/intechopen.92044 ◽

2020 ◽

Author(s):

Saud Altaf ◽

Shafiq Ahmad

Keyword(s):

Fault Diagnosis ◽

Health Monitoring ◽

Power Line ◽

Noisy Signal ◽

Industrial Environment ◽

Power Bus ◽

Wide Range ◽

Machine Health Monitoring ◽

Machine Health ◽

Line Network

The machinery arrangements in industrial environment normally consist of motors of diverse sizes and specifications that are provided power and connected with common power-bus. The power-line could be act as a good source for travelling the signal through power-line network and this can be leave a faulty symptom while inspection of motors. This influence on other neighbouring motors with noisy signal that may present some type of fault condition in healthy motors. Further intricacy arises when this type of signal is propagated on power-line network by motors at different slip speeds, power rating and many faulty motors within the network. This sort of convolution and diversification of signals from multiple motors makes it challenging to measure and accurately relate to a certain motor or specific fault. This chapter presents a critical literature review analysis on machine-fault diagnosis and its related topics. The review covers a wide range of recent literature in this problem domain. A significant related research development and contribution of different areas regarding fault diagnosis and traceability within power-line networks will be discussed in detail throughout this chapter.

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A Study of Convolutional Neural Networks Learning Mechanisms for Machine Health Monitoring Applications

Annual Conference of the PHM Society ◽

10.36001/phmconf.2020.v12i1.1196 ◽

2020 ◽

Vol 12 (1) ◽

pp. 8

Author(s):

Mohammed Alabsi ◽

Ahmad Saeed Mohammad ◽

Ahmed Sammoud

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Health Monitoring ◽

Band Pass Filter ◽

Learning Mechanisms ◽

Pass Filter ◽

Wide Range ◽

Machine Health Monitoring ◽

Machine Health ◽

Analytical Approaches

In recent years, Deep Learning (DL) and Internet of Things (IoT) technologies have been used and deployed jointly to solve a wide range of modern technical challenges in different areas. With the continuous advancement of IoT connectivity solutions, the range of applications that can benefit from such an increase is limitless. One area that can benefit significantly from the combined strength of DL and IoT technologies is Machine Health Monitoring (MHM) Systems. MHM utilizes different analytical approaches and tools to determine the state and health of different components in running machinery. The traditional MHM system uses control limits from predetermining values that determine if a component has failed depending on the preset limits of the machinery. The main disadvantage of using such technique us the unpredictable nature of the timing and component failure. This type of failure causes unplanned production time loss and increases the cost of maintenance due to the unpredictability of the failure events. With DL and low-cost sensors that use different IoT connectivity solutions, MHM systems can utilize the learning capabilities of the DL network to perform end-to-end prognosis. One crucial fact is that features learned by Deep Neural Networks (DNN) are part of a large black box, and there are valuable underlying physical meanings embedded within the features. Hence, there is an exciting research area to explore underlying mechanisms and interpret physical meanings within DNN. In this paper, DNN learning mechanisms are evaluated using three different models: stacked autoencoders (SAE), denoising autoencoders (DAE), and convolutional neural networks (CNN). Initial results indicate that the input layer behaves similarly to a band-pass filter. However, deep layers require optimal input design to maximize neuron activation, which leads to an extensive understanding of deep layer learning consequently (In progress).

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LSTM-GAN-AE: A Promising Approach for Fault Diagnosis in Machine Health Monitoring

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3135328 ◽

2021 ◽

pp. 1-1

Author(s):

Haoqiang Liu ◽

Hongbo Zhao ◽

Jiayue Wang ◽

Shuai Yuan ◽

Wenquan Feng

Keyword(s):

Fault Diagnosis ◽

Health Monitoring ◽

Machine Health Monitoring ◽

Machine Health

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Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Entropy ◽

10.3390/e21060540 ◽

2019 ◽

Vol 21 (6) ◽

pp. 540 ◽

Cited By ~ 2

Author(s):

Nibaldo Rodriguez ◽

Lida Barba ◽

Pablo Alvarez ◽

Guillermo Cabrera-Guerrero

Keyword(s):

Fault Diagnosis ◽

Shannon Entropy ◽

Health Monitoring ◽

Wavelet Packet ◽

Rolling Element Bearing ◽

Permutation Entropy ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Machine Health Monitoring ◽

Machine Health

Bearing fault diagnosis methods play an important role in rotating machine health monitoring. In recent years, various intelligent fault diagnosis methods have been proposed, which are mainly based on the features extraction method combined with either shallow or deep learning methods. During the last few years, Shannon entropy features have been widely used in machine health monitoring, improving the accuracy of the bearing fault diagnosis process. Therefore, in this paper, we consider the combination of multi-scale stationary wavelet packet analysis with the Fourier amplitude spectrum to obtain a new discriminative Shannon entropy feature that we call stationary wavelet packet Fourier entropy (SWPFE). Features extracted by our SWPFE method are then passed onto a shallow kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. The proposed method was applied on two experimental vibration signal databases of a rolling element bearing and compared to two recently proposed methods called stationary wavelet packet permutation entropy (SWPPE) and stationary wavelet packet dispersion entropy (SWPPE). Based on our results, we can say that the proposed method is able to achieve better accuracy levels than both the SWPPE and SWPDE methods using fewer failure features. Further, as our method does not require any hyperparameter calibration step, it is less dependent on user experience/expertise.

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