Data-driven based fault prognosis for industrial systems: a concise overview

Zhong, Kai; Han, Min; Han, Bing

doi:10.1109/jas.2019.1911804

Cited by 108 publications

(33 citation statements)

References 101 publications

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“…Although sometimes it is thought that implementing these solutions consists only of selecting and optimizing a Machine Learning algorithm, many other operations have to be carried out in order to develop efficient approaches. As Figure 7 shows, this methodology can be split in four main stages, which namely are the acquisition and raw data organization steps, the raw data preprocessing step, the Machine Learning model design and the implementation and integration in the application [43][44][45].…”

Section: Fundamentals Of the Machine Learning Workflowmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

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Section: Fundamentals Of the Machine Learning Workflowmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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“…The fault prognosis task for industrial processes is necessary to estimate or predict the process operation time or location before a certain fault or an abnormal change occurs and a suitable action, e.g., a preventive maintenance task, is taken. So, owing to the increasing dynamics and complexity in many real industrial systems, the development of effective fault prognosis methods is receiving great attention from diverse process industries [149].…”

Section: Fault Prognosismentioning

confidence: 99%

“…Zhong et al [149] reviewed different types of data-driven fault prognosis approaches for various industrial processes. In this overview, they first introduced critical issues as well as unique characteristics of several data-driven fault prognosis approaches and also addressed the links between fault prognosis methods and fault diagnosis methods.…”

Section: Fault Prognosismentioning

confidence: 99%

“…Finally, in the era of big data, the real-time and comprehensive FDD strategies using all the information should be developed. In conclusion, this review would be helpful for both practitioners and researchers in this field to comprehend the key characteristics, applications, and future challenges of the process monitoring and FDD approaches [149].…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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A Review on Fault Detection and Process Diagnostics in Industrial Processes

2020

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The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.

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“…Lu Wei et al [18] proposed a new normal behavior modeling (NBM) method to predict wind turbine electric pitch system failures using supervisory control and data acquisition (SCADA) information. K. Zhong et al [19] presented a systematic overview of data-driven fault prognosis for industrial systems. According to different data characteristics, corresponding failure prediction methods are explained.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Fault Diagnosis of Imbalanced Text Mining for CTCS-3 Data Preprocessing

Shi

Zhang

2021

Sustainability

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At present, the method for fault diagnosis and maintenance of the CTCS-3 (Chinese Train Control System Level 3) electronic equipment relies too heavily on expert knowledge. Moreover, the use of historical fault data is not valued. This paper proposes a sustainable fault diagnosis model based on imbalanced text mining. First, to process fault data from the field recorded in natural language, natural language processing technology is used to extract fault feature words. Then, a term frequency-inverse document frequency model is used to transform the fault feature words extracted from the database into vectors. It is worth noting that imbalance in the fault samples affects the accuracy of this sustainable fault diagnosis model. To solve this problem, we use the borderline-synthetic minority over-sampling technique in the step of predicting train fault components, we also use the backpropagation neural network we proposed and the naive Bayesian model which is commonly used as a classification model, to compare the prediction accuracy of these two algorithms. The experimental results perform well, which proves that the fault diagnosis method using the backpropagation neural network can further assist engineers to complete timely repair and maintenance work. The research in this paper has played a very important role in technical support for intelligent train dispatching and command, and will also play a positive role in technical support for the automatic operation of urban rail transit under the prevention and control of the new coronavirus.

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Data-driven based fault prognosis for industrial systems: a concise overview

Cited by 108 publications

References 101 publications

Data-Driven Fault Diagnosis for Electric Drives: A Review

Data-Driven Fault Diagnosis for Electric Drives: A Review

A Review on Fault Detection and Process Diagnostics in Industrial Processes

Sustainable Fault Diagnosis of Imbalanced Text Mining for CTCS-3 Data Preprocessing

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