Intelligent condition based monitoring of rotating machines using sparse auto-encoders

Verma, Nishchal K.; Gupta, Vishal K.; Sharma, Mayank; Sevakula, Rahul Kumar

doi:10.1109/icphm.2013.6621447

Cited by 85 publications

(39 citation statements)

References 7 publications

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“…After that, a lot of researches about deep learning [40] have been studied in many fields like computer vision [18], speech recognition [19], information retrieval [20] and traffic control [21] over the past 10 years. Many researchers also have applied deep learning in fault diagnosis and condition recognition [22][23][24][25][26][27][28]. Verma et al [24] proposed an intelligent condition monitoring method for rotating machine using sparse auto-encoder.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers also have applied deep learning in fault diagnosis and condition recognition [22][23][24][25][26][27][28]. Verma et al [24] proposed an intelligent condition monitoring method for rotating machine using sparse auto-encoder. Jia et al [25] proposed a novel intelligent diagnosis method based on deep neural networks to implement both fault feature extraction and intelligent diagnosis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

Zhang

Gao

Wen

et al. 2017

Microelectronics Reliability

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

Zhang

Gao

Wen

et al. 2017

Microelectronics Reliability

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“…This simplified the architecture and minimized both training and processing time. Fault diagnosis for other mechanical systems was addressed by others: the logistic regression method for a real elevator door system [112]; a Naïve Bayes scheme and Bayes net approach to monoblock centrifugal pump systems [113]; SAE for rotating machines and hydraulic pumps [114,115]; PKFA for machine tools [116] and the framework, DNN for tidal turbine generators [117], the DL method for wind generators [118], and CNNs for a structural damage detection system [119]. In addition, these above-mentioned references were displayed several problem-solving revolutions of mechanical components.…”

Section: Other Mechanical Componentsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

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“…The performance of DNN has been state of the art in many applications, such as computer vision and natural language process [23,24]. Researchers have applied DNN in fault diagnosis as well [25][26][27][28]. Verma et al [27] purposed a condition monitoring method using sparse autoencoder.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have applied DNN in fault diagnosis as well [25][26][27][28]. Verma et al [27] purposed a condition monitoring method using sparse autoencoder. In [25], Tagawa et al built a model based on denoising autoencoder for car fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

Xie

Zhang

2017

Shock and Vibration

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The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

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Intelligent condition based monitoring of rotating machines using sparse auto-encoders

Cited by 85 publications

References 7 publications

A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

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