Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review

The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

IoT Connectivity Technologies and Applications: A Survey

The working condition of rolling bearing usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of the dependence on manual feature design, the deep CNN model is initialized. Secondly, to adapt to different signal characteristics, the main parameters of deep CNN model are determined with particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to analyze the rolling bearing vibration signals collected from an experimental setup and electrical locomotive, and the analysis results confirm that the proposed method has more accurate and robust performance than other intelligent methods.

https://iopscience.iop.org/article/10.1088/1361-6501/aa6e22/pdf

An adaptive deep convolutional neural network for rolling bearing fault diagnosis

[Treatment of Cushing's disease].

Intelligent fault diagnosis techniques cross rotating machines have great significances in theory and engineering For this purpose, this paper presents a novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO). First, novel stacked auto-encoder (NSAE) model is designed with scaled exponential linear unit (SELU), correntropy and nonnegative constraint. Then, NSTAE is constructed using NSAE and parameter transfer strategy to enable the pre-trained source-domain NSAE to adapt to the target-domain samples. Finally, PSO is used to flexibly decide the hyperparameters of NSTAE. The effectiveness and superiority of the presented method are investigated through analyzing the collected experimental data of bearings and gears from different rotating machines.

Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO.

The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method.

Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks

The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference. Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

Induction motor stator fault detection via fuzzy logic

World population growing in conjunction with the preference to live in the cities; make the city management a challenging issue. Traditional Cities with their common features will not be able to handle the human needs. As a result, smart city and its beneficial outcomes will attract a lot of attention recently. In fact, Smart city/community that is the field of collecting and processing data from so many different areas while making proper decisions and feeding to all parts of the system will be the future style of the cities. In other words, smart community is a complex big data problem which should combine different fields of research to make a unit environment. This brief survey will deal with all aspects of the smart communities. There will also be explained different categories of the smart communities in addition to their future challenges. In this work we try to introduce some aspects of the smart transportation in more detail.

A Brief Survey on Smart Community and Smart Transportation

“Distress” or a substantial amount of stress may decrease brain functionality and cause neurological disorders. On the other hand, very low cognitive arousal may affect one’s concentration and awareness. Data collected using wrist-worn wearable devices, in particular, skin conductance data, could be used to look into one’s cognitive-stress-related arousal. Our goal here is to present excitatory and inhibitory wearable machine-interface (WMI) architectures to control one’s cognitive-stress-related arousal state. We first present a model for skin conductance response events as a function of environmental stimuli associated with cognitive stress and relaxation. Then, we perform Bayesian filtering to estimate the hidden cognitive-stress-related arousal state. We finally close the loop using fuzzy control. In particular, we design two classes of controllers for our WMI architectures: (1) an inhibitory controller for reducing arousal and (2) an excitatory controller for increasing arousal. Our results illustrate that our simulated skin conductance responses are in agreement with experimental data. Moreover, we illustrate that our fuzzy control can successfully have both inhibitory and excitatory effects and regulate one’s cognitive stress. In conclusion, in a simulation study based on experimental data, we have illustrated the feasibility of designing both excitatory and inhibitory WMI architectures. Since wearable devices can be used conveniently in one’s daily life, the WMI architectures have a great potential to regulate one’s cognitive stress seamlessly in real-world situations.

/pdf/state-space-modeling-and-fuzzy-feedback-control-of-cognitive-1r3ebo4dtj.pdf

State-Space Modeling and Fuzzy Feedback Control of Cognitive Stress

The detection of faults in induction motors is becoming increasingly important The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor A fuzzy logic approach may help to diagnose induction motor faults This work presents a reliable method for the detection of stator winding faults based on monitoring the line current amplitudes In this method, fuzzy logic is used to make decisions about the stator motor condition In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis The motor condition is described using linguistic variables Fuzzy subsets and the corresponding membership functions describe stator current amplitudes A knowledge base, comprising rule and data bases, is built to support the fuzzy inference The induction motor condition is diagnosed using a compositional rule of fuzzy inference Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis

Hamid Fekri Azgomi

Papers

Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks

Induction motor stator fault detection via fuzzy logic

A Brief Survey on Smart Community and Smart Transportation

State-Space Modeling and Fuzzy Feedback Control of Cognitive Stress

Experimental validation on stator fault detection via fuzzy logic