A new approach to intelligent fault diagnosis of rotating machinery

Lei, Yaguo; He, Zhengjia; Ye, Zi

doi:10.1016/j.eswa.2007.08.072

Cited by 333 publications

(174 citation statements)

References 22 publications

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“…Twenty-three features were extracted from the raw data in the present study (Table 2). These indices were the feature functions that have been used by other researchers in the field of data mining and can be used to obtain the information required for data mining (Khazaee et al, 2013;Lei et al, 2008). In this table, x(n) is signal time-series and N is the number of data points.…”

Section: Feature Extractionmentioning

confidence: 99%

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Sadeghi

Banakar

Khazaee

et al. 2015

Rev. Bras. Cienc. Avic.

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In this study, an intelligent method was implemented for the detection and classification of chickens by infected Clostridium perfringens type A based on their vocalization. To this aim, the birds were first divided into two groups that were placed in separate cages with 15 chickens each. Chickens were inoculated with Clostridium perfringens type A on day 14. In order to ensure the absence of secondary diseases and their probable effect on bird vocalization, vaccines for common diseases were administered. During 30 days of the experiment, chicken vocalization was recorded every morning at 8 a.m. using a microphone and a data collection card under equal and controlled conditions. Sound signals were investigated in time domains, and 23 features were selected. Using Fisher Discriminate Analysis (FDA), five of the most important and effective features were chosen. Neural Network Pattern Recognition (NNPR) structure with one hidden layer was applied to detect signals and classifying healthy and unhealthy chickens. Firstly, this neural network was trained with 34 samples, after which eight samples were tested for accuracy. Classification accuracy was 66.6 and 100% for days 16 and 22; i.e., two and eight days after the disease, respectively. The results of this study demonstrated the usefulness and effectiveness of intelligent methods for diagnosing diseases in chickens.

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Section: Feature Extractionmentioning

confidence: 99%

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Sadeghi

Banakar

Khazaee

et al. 2015

Rev. Bras. Cienc. Avic.

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“…Song et al [253] proposed an intelligent condition diagnosis method for rotating machineries using the probability density analysis and the canonical discriminant analysis. Lei et al [254] presented a new intelligent fault diagnosis approach based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system, which they then applied in fault diagnosis of rolling element bearings.…”

Section: Statistical Approachesmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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“…As a result of proper process monitoring, downtime is minimized, safety of plant operations is improved, and manufacturing costs are reduced [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Based on Graph Theory and Linear Discriminant Principle in Electric Power Network

Zhang¹,

Ma²,

Zhang³

et al. 2010

WSN

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In this paper, we adopt a novel topological approach to fault diagnosis. In our researches, global information will be introduced into electric power network, we are using mainly BFS of graph theory algorithms and linear discriminant principle to resolve fast and exact analysis of faulty components and faulty sections, and finally accomplish fault diagnosis. The results of BFS and linear discriminant are identical. The main technical contributions and innovations in this paper include, introducing global information into electric power network, developing a novel topological analysis to fault diagnosis. Graph theory algorithms can be used to model many different physical and abstract systems such as transportation and communication networks, models for business administration, political science, and psychology and so on. And the linear discriminant is a procedure used to classify an object into one of several a priori groupings dependent on the individual characteristics of the object. In the study of fault diagnosis in electric power network, graph theory algorithms and linear discriminant technology must also have a good prospect of application.

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A new approach to intelligent fault diagnosis of rotating machinery

Cited by 333 publications

References 22 publications

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Basic research on machinery fault diagnostics: Past, present, and future trends

Fault Diagnosis Based on Graph Theory and Linear Discriminant Principle in Electric Power Network

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