Application Research of Kalman Filter and SVM Applied to Condition Monitoring and Fault Diagnosis

Li, Ké; Zhang, Yue Lei; Li, Zhi Xiong

doi:10.4028/www.scientific.net/amm.121-126.268

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…The classifier is used to classify the results of feature extraction and transformation, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) are commonly used. For example, Cheng et al [14,15] extracted the feature of roller bearing vibration signals by an improved Local Mean Decomposition (LMD) method, and used SVM as classifier to diagnose bearing faults. Yuan et al [16] extracted the eigenvalue of hydraulic oil pressure as the input of a BP network to obtain diagnosis results.…”

Section: Introductionmentioning

confidence: 99%

Signal Status Recognition Based on 1DCNN and Its Feature Extraction Mechanism Analysis

Huang

Tang

Dai

et al. 2019

Sensors

101

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In this paper, we construct a one-dimensional convolutional neural network (1DCNN), which directly takes as the input the vibration signal in the mechanical operation process. It can realize intelligent mechanical fault diagnosis and ensure the authenticity of signal samples. Moreover, due to the excellent interpretability of the 1DCNN, we can explain the feature extraction mechanism of convolution and the synergistic work ability of the convolution kernel by analyzing convolution kernels and their output results in the time-domain, frequency-domain. What’s more, we propose a novel network parameter-optimization method by matching the features of the convolution kernel with those of the original signal. A large number of experiments proved that, this optimization method improve the diagnostic accuracy and the operational efficiency greatly.

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Section: Introductionmentioning

confidence: 99%

Signal Status Recognition Based on 1DCNN and Its Feature Extraction Mechanism Analysis

Huang

Tang

Dai

et al. 2019

Sensors

101

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“…Many existing methods account for part of the uncertainty, e.g. methods based on Kalman filters [1][2][3][4] or methods based on set-membership approaches [5,6]. Such methods however adopt strong assumptions regarding the type of uncertainty present, and require that the system can be described by a specific model, often a linear state space model.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis–A comparative study

Verbert

Babuka

Schutter

2017

Engineering Applications of Artificial Intelligence

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Even though various frameworks exist for reasoning under uncertainty, a realistic fault diagnosis task does not fit into any of them in a straightforward way. For each framework, only part of the available data and knowledge is in the desired format. Moreover, additional criteria, like clarity of inference and computational efficiency, require trade-offs to be made. Finally, fault diagnosis is usually just a subpart of a larger process, e.g. condition-based maintenance. Consequently, the final goal of fault diagnosis is not (just) decision making, and the outcome of the diagnosis process should be a suitable input for the subsequent reasoning process. In this chapter, we analyze how a knowledge-based diagnosis task is influenced by uncertainty, investigate which additional objectives are of relevance, and compare how these characteristics and objectives are handled in two well-known frameworks, namely the Bayesian and the Dempster-Shafer reasoning framework. In contrast to previous works, which take the reasoning method as the starting point, we start from the application, knowledge-based fault diagnosis, and examine the effectiveness of different reasoning methods for this specific application. It is concluded that the suitability of each reasoning method highly depends on the problem under consideration and on the requirements of the user. The best framework can only be assigned given that the problem (including uncertainty characteristics) and the user requirements are completely known.

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“…Similarly, Widodo et al [120] applied a multi-class SVM classifier for the fault diagnostics of induction motors. In a bearing system application, Li et al [121] performed fault diagnosis on rolling bearings using a SVM based classifier.…”

Section: Classification and Health Stage Division As Ml-driven Diagnomentioning

confidence: 99%

Achieving a representation of asset data conducive to machine learning driven predictive maintenance

Aremu¹

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The success of an operation in industries such as mining, aviation, and industrial manufacturing is heavily dependent on the continuous functioning of complex systems. These complex systems are often considered critical physical assets and their reliability is significantly correlated to a successful and productive operation. The unexpected failure of an asset results in high maintenance costs, loss in profits, reduced human safety, and hazardous environmental impacts.

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Application Research of Kalman Filter and SVM Applied to Condition Monitoring and Fault Diagnosis

Cited by 13 publications

References 8 publications

Signal Status Recognition Based on 1DCNN and Its Feature Extraction Mechanism Analysis

Signal Status Recognition Based on 1DCNN and Its Feature Extraction Mechanism Analysis

Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis–A comparative study

Achieving a representation of asset data conducive to machine learning driven predictive maintenance

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