Long short-term memory neural network based fault detection and isolation for electro-mechanical actuators

Yang, Jing; Guo, Yingqing; Zhao, Wanli

doi:10.1016/j.neucom.2019.06.029

Cited by 78 publications

(30 citation statements)

References 30 publications

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“…over the past ten years [39], and it has worked perfectly in speech recognition [40] and text classification [41,42]. Reference [43] shows fault prediction to be the main subject in nonlinear systems [44]. ANN-based methods are alternative ways of predicting COVID-19 outbreak.…”

Section: The Possible Platform To Accelerate Conventional Methodsmentioning

confidence: 99%

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

et al. 2020

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COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

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Section: The Possible Platform To Accelerate Conventional Methodsmentioning

confidence: 99%

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

et al. 2020

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“…In [ 61 ], Huang et al adopted a BLSTM-based method to solve the prognostic problem of aircraft engine remaining useful life (RUL). In [ 62 ], Yang et al proposed a method for FD for aircraft electromechanical actuators that used LSTM to analyze the correlation of sensors. In [ 63 ], an aircraft engine degradation assessment and RUL prediction framework based on LSTM was proposed by Miao et al…”

Section: Related Workmentioning

confidence: 99%

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao

Wei

Zhou

et al. 2020

Sensors

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In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

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“…In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts were made for the application of intelligent algorithms [66,67] including new approaches to Fault Detection and Isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69][70][71][72][73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77]. In addition, a technician needs knowledge and a good amount of experience to correctly use such sensors [78][79][80][81][82][83][84].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Long short-term memory neural network based fault detection and isolation for electro-mechanical actuators

Cited by 78 publications

References 30 publications

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

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