Fault Diagnosis System for Rotary Machine Based on Fuzzy Neural Networks

Zhang, Sheng; Asakura, Toshiyuki; Xu, Xiaoli; Xu, Bo

doi:10.1299/jsmec.46.1035

Cited by 53 publications

(7 citation statements)

References 5 publications

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“…Time domain analysis has the disadvantages of low sensitivity and low accuracy, but its simple calculations and direct signal processing contribute to shortening of the processing time. Simple time domain method is not suitable for effective fault diagnosis, but it is much better when combined with other approaches, for example, neural network [7], pattern recognition, and artificial intelligence. Muralidharan et al [8] finished fault diagnosis of self-aligning carrying idler in different conditions, by using statistical measures to get useful features and then to classify them with decision tree algorithm.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

et al. 2016

Shock and Vibration

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Vibration signal analysis is one of the most effective methods for mechanical fault diagnosis. Available part of the information is always concealed in component noise, which makes it much more difficult to detect the defection, especially at early stage of the development. This paper presents a new approach for mechanical fault diagnosis based on time domain analysis and adaptive fuzzyC-means clustering. By analyzing vibration signal collected, nine common time domain parameters are calculated. This lot of data constitutes data matrix as characteristic vectors to be detected. And using adaptive fuzzyC-means clustering, the optimal clustering number can be gotten then to recognize different fault types. Moreover, five parameters, including variance, RMS, kurtosis, skewness, and crest factor, of the nine are selected as the new eigenvector matrix to be clustered for more optimal clustering performance. The test results demonstrate that the proposed approach has a sensitive reflection towards fault identifications, including slight fault.

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Section: Introductionmentioning

confidence: 99%

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

et al. 2016

Shock and Vibration

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“…Among them, the paradigms of the Neural Networks, the Fuzzy Logic, the Expert Systems and the Genetic Algorithms have been widely used [ 24 , 25 , 28 , 29 ]. In this paper, effort will be focused on the Neuro-Fuzzy systems [ 30 , 31 ]. This paradigm could be considered a hybrid approach between Neural Networks and the Fuzzy Systems.…”

Section: Artificial Intelligence Approachmentioning

confidence: 99%

An Artificial Intelligence Approach for Gears Diagnostics in AUVs

Marichal

Castillo

López

et al. 2016

Sensors

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In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

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“…Fuzzy logic is also incorporated with other techniques such as neural networks and ES for fault diagnostic application. For example, Zhang et al [78] developed an FNN for fault diagnosis of rotary machines to improve the recognition rate of pattern recognition, especially in the case that sample data are similar. Lou and Loparo [79] employed an adaptive neural-fuzzy inference system as a diagnostic classifier for bearing fault diagnosis.…”

Section: Pattern Recognition-based Approachesmentioning

confidence: 99%

Machine Fault Diagnosis and Prognosis: The State of The Art

Tran¹,

Yang²

2009

International Journal of Fluid Machinery and Systems

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Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

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Fault Diagnosis System for Rotary Machine Based on Fuzzy Neural Networks

Cited by 53 publications

References 5 publications

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

An Artificial Intelligence Approach for Gears Diagnostics in AUVs

Machine Fault Diagnosis and Prognosis: The State of The Art

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