A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

Jiang, Wen; Hu, Weiwei; Xie, Chunhe

doi:10.3390/app7030280

Cited by 31 publications

(33 citation statements)

References 70 publications

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“…The data-driven technique does not require the creation of a physical model of the device; use the monitored data during the operation of the equipment to diagnose the fault type of the equipment, for example, machine learning, [19][20][21][22] multivariate statistical analysis, 23 signal processing, 24 rough set, 25,26 fuzzy set, 27 and multi-sensors or multi-sources information fusion method. [28][29][30][31] In the multi-sensors information fusion based method, in which the data of multiple sensors (or sources) are fused, reflects the diversity, redundancy, and complementarity of multiple information. Therefore, this method could obtain more reliable diagnostic results than single source information.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method based on time domain weighted data aggregation and information fusion

Zhang

Jiang

Deng

2019

International Journal of Distributed Sensor Networks

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Fault diagnosis of equipment is a key issue in the industrial field, and it is essential to keep abreast of equipment status. However, previous studies either considered fault data at a single moment or gave the same weight to data over a period of time. In view of the problems above, fault diagnosis method based on time domain weighted data aggregation and information fusion is proposed in this article. First, the monitored data of sensors loaded by the equipment are aggregated utilizing the linear decaying weights. Then, Gaussian models of each fault type under different fault features are established based on aggregated data. And the basic probability assignments are generated by matching aggregated testing samples with the constructed Gaussian model. At last, the basic probability assignments generated under each fault feature are fused by Dempster combination rule. The proposed method is verified and the results show that the total fault recognition rate can reach 97.5%, which increased by 1.9% compared with the method that Gaussian model constructed by original data.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis method based on time domain weighted data aggregation and information fusion

Zhang

Jiang

Deng

2019

International Journal of Distributed Sensor Networks

Self Cite

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“…Compared with Bayesian probability theory, DS theory does not need to know the prior probability, and has the ability to directly express "uncertainty" and "do not know". Reference [13] proposed a diagnostic method of multi-sensor data fusion based on DS theory for uncertainty modeling. Reference [14] solved the problem of low accuracy and reliability in the diagnosis of single sensor by means of DS theory.…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

Yao¹,

Li²,

Yao³

et al. 2018

Int. J. Onl. Eng.

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Abstract-As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

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“…This type of research mainly uses machine learning algorithms to diagnose fault parameters based on data characteristics. Conventional fault diagnosis methods include BP neural network and SVM support vector machine [5][6][7]. For example, Cai et al [5] propose a new fault diagnosis method for marine diesel engine system.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis of a Marine Fuel System Based on the SaDE-ELM Algorithm

Wei

Yue

2018

Algorithms

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Since the traditional fault diagnosis method of the marine fuel system has a low accuracy of identification, the algorithm solution can easily fall into local optimum, and they are not fit for the research on the fault diagnosis of a marine fuel system. Hence, a fault diagnosis method for a marine fuel system based on the SaDE-ELM algorithm is proposed. First, the parameters of initializing extreme learning machine are adopted by a differential evolution algorithm. Second, the fault diagnosis of the marine fuel system is realized by the fault diagnosis model corresponding to the state training of marine fuel system. Based on the obtained fault data of a marine fuel system, the proposed method is verified. The experimental results show that this method produces higher recognition accuracy and faster recognition speed that are superior to the traditional BP neural network, SVM support vector machine diagnosis algorithm, and the un-optimized extreme learning machine algorithm. The results have important significance relevant to fault diagnosis for a marine fuel system. The algorithm based on SaDE-ELM is an effective and practical method of fault diagnosis for a marine fuel system.

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A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

Cited by 31 publications

References 70 publications

Fault diagnosis method based on time domain weighted data aggregation and information fusion

Fault diagnosis method based on time domain weighted data aggregation and information fusion

Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

Research on Fault Diagnosis of a Marine Fuel System Based on the SaDE-ELM Algorithm

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