Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

Peng, Bo; Wan, Shuting; Bi, Ying; Xue, Bing; Zhang, Mengjie

doi:10.1109/tcyb.2020.3032945

Cited by 68 publications

(21 citation statements)

References 65 publications

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“…In particular, future works should aim to construct system-level features whose values remain in a given range when the system works under the same operating condition even if its components are degrading. A promising tool to construct the features that respect this property is represented by Genetic Programming, which can also be used to find optimal component-level features and HIs from the same set of features extracted from raw signals [86].…”

Section: Discussionmentioning

confidence: 99%

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

et al. 2021

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Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals.

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Section: Discussionmentioning

confidence: 99%

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

et al. 2021

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“…Some studies [26][27][28][29][30][31] also describe the fusion of different fault diagnosis technologies, which have achieved good results in certain application fields. At the same time, with regard to fault feature extraction, there have been many improved algorithms that eliminate the dependence on domain knowledge and expert experience [32,33]; and as result, great breakthroughs have been made in the automatic extraction of mechanical fault features. However, due to increasingly prominent complex nonlinear and strong interference problems, there is no immutable general model for fault diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A Fault Diagnosis and Visualization Method for High-Speed Train Based on Edge and Cloud Collaboration

et al. 2021

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Safety is the most important aspect of railway transportation. To ensure the safety of high-speed trains, various train components are equipped with sensor devices for real-time monitoring. Sensor monitoring data can be used for fast intelligent diagnosis and accurate positioning of train faults. However, existing train fault diagnosis technology based on cloud computing has disadvantages of long processing times and high consumption of computing resources, which conflict with the real-time response requirements of fault diagnosis. Aiming at the problems of train fault diagnosis in the cloud environment, this paper proposes a train fault diagnosis model based on edge and cloud collaboration. The model first utilizes a SAES-DNN (stacked auto-encoders deep neural network) fault recognition method, which can integrate automatic feature extraction and type recognition and complete fault classification over deep hidden features in high-dimensional data, so as to quickly locate faults. Next, to adapt to the characteristics of edge computing, the model applies a SAES-DNN model trained in the cloud and deployed in the edge via the transfer learning strategy and carries out real-time fault diagnosis on the vehicle sensor monitoring data. Using a motor fault as an example, when compared with a similar intelligent learning model, the proposed intelligent fault diagnosis model can greatly improve diagnosis accuracy and significantly reduce training time. Through the transfer learning approach, adaptability of the fault diagnosis algorithm for personalized applications and real-time performance of the fault diagnosis is enhanced. This paper also proposes a visual analysis method of train fault data based on knowledge graphs, which can effectively analyze fault causes and fault correlation.

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“…It is difficult to diagnose the fault of mechanical equipment directly through vibration signals [8]. Therefore, it is necessary to extract the characteristics of vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Role of entropy in fault diagnosis of mechanical equipment: a review

Wang,

Sun

2023

Eng. Res. Express

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Mechanical equipment is widely used in daily life and production manufacturing, and it is an indispensable part of modern society. Fault diagnosis of mechanical equipment can effectively diminish catastrophic failures and significant economic losses. How to evaluate their status is the key problem of safe operation. The monitoring data of mechanical equipment obtained by sensors can be used to analyze potential problems and fault information. The vibration signal is the most easily obtained and commonly used monitoring data of mechanical equipment. But the vibration signal is unstable and usually shows nonlinear characteristics in actual measurement, which makes it difficult to extract fault features. Entropy can quantify the complexity of time series and detect the dynamic change of nonlinear behavior. Relying on the performance of entropy, it can be used as an effective tool for dynamic characteristics and applied to the fault diagnosis of mechanical equipment. The purpose of this paper is to summarize the related research of mechanical equipment fault diagnosis based on entropy methods in recent five years. Different entropy methods are classified, and specific application methods are introduced. On this basis, these entropy methods are deeply discussed. The gaps filled by different entropy methods in mechanical equipment fault diagnosis are discussed. The problems faced by entropy methods in the application of mechanical equipment fault diagnosis are also discussed. Theoretical and engineering guidance of mechanical fault diagnosis research is provided, which is of great significance.

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Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

Cited by 68 publications

References 65 publications

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

A Fault Diagnosis and Visualization Method for High-Speed Train Based on Edge and Cloud Collaboration

Role of entropy in fault diagnosis of mechanical equipment: a review

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