Multi-Index Fusion-Based Fault Diagnosis Theories and Methods

Wu, Xing; Chen, J.; Wang, W.; Zhou, Yonghui

doi:10.1006/mssp.1999.1279

Cited by 13 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the influence of feature extraction is obviously inferior to the pattern recognition strategy when there are many fault types. Therefore, the focus of this paper is to develop a multistage classification method, and this paper only extracts six typical time-domain and four typical frequency-domain characteristics, [36][37][38] including root mean square, kurtosis index, peak index, margin index, impulse factor, waveform index, mean frequency, frequency centroid, root mean square of frequency, and standard deviation of frequency, as the fault features. Root mean square can reflect the vibration energy and discrete characteristics of signal.…”

Section: The Fault Diagnosis Approach For Diesel Enginementioning

confidence: 99%

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

Liu

Zhang

Qin

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

Diesel engine is the most widely used power source of machines. However, faults occur frequently and often cause terrible accidents and serious economic losses. Therefore, diesel engine fault diagnosis is very important. Commonly, a single unitary pattern recognition method is used to diagnose the faults of diesel engine, but its performance decreases sharply when there are many fault types. Targeting this problem, a novel diesel engine fault diagnosis approach is proposed in this study. The approach is composed of four stages. Firstly, the nonstationary and nonlinear vibration signal of diesel engine is decomposed into a series of proper rotation components (PRCs) and a residual signal by the intrinsic time-scale decomposition (ITD) method. Secondly, six typical time-domain and four typical frequency-domain characteristics of the first several PRCs are extracted as fault features. Then, the modular and ensemble concepts are introduced to construct the multistage Adaboost relevance vector machine (RVM) model, in which the kernel fuzzy c-means clustering (KFCM) algorithm is used to decompose a complex classification task into several simple modules, and the Adaboost algorithm is used to improve the performance of each RVM based module. Finally, the fault diagnosis results can be obtained by inputting the fault features into the multistage Adaboost RVM model. The analysis results show that the fault diagnosis approach based on ITD and multistage Adaboost RVM performs effectively for the fault diagnosis of diesel engine, and it is better than the traditional pattern recognition methods.

show abstract

Section: The Fault Diagnosis Approach For Diesel Enginementioning

confidence: 99%

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

Liu

Zhang

Qin

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…But it is difficult to distinguish both forms of singularities by using one of them. Following the idea of information fusion theory [10], a comprehensive singularity detection criterion, which considers both λ 1 and E, was designed. In view of the powerful capacity of the kernel principal component analysis (KPCA) on discrimination [11], the KPCA was adopted for this application.…”

Section: Design Of a Composite Singularity Detection Criterionmentioning

confidence: 99%

Detecting the singularities in engineering signals

Yang

Hull

Seymour³

2006

Journal of Materials Processing Technology

View full text Add to dashboard Cite

“…Some interesting algorithms are proposed in [85][86][87][88][89]. Another important aspect is to identify the detection (inspection) intervals, optimization of cost and replacement decision-making.…”

Section: Statistical Fdi/maintenancementioning

confidence: 99%

System health monitoring and prognostics — a review of current paradigms and practices

Kothamasu

Huang

VerDuin

2006

Int J Adv Manuf Technol

275

121

View full text Add to dashboard Cite

System health monitoring is a set of activities performed on a system to maintain it in operable condition. Monitoring may be limited to the observation of current system states, with maintenance and repair actions prompted by these observations. Alternatively, monitoring of current system states is being augmented with prediction of future operating states and predictive diagnosis of future failure states.Such predictive diagnosis or prognosis is motivated by the need for manufacturers and other operators of complex systems to optimize equipment performance and reduce costs and unscheduled downtime. Prognosis is a difficult task requiting precise, adaptive and intuitive models to predict future machine health states. Numerous modeling techniques have been proposed in the literature and implemented in practice. This paper reviews the philosophies and techniques that focus on improving reliability and reducing unscheduled downtime by monitoring and predicting machine health.Keywords Condition-based maintenance 9 Health monitoring -Model-based diagnosis -OSA-CBM 9 Preventive maintenance 9 Reliability-centered maintenance

show abstract

Multi-Index Fusion-Based Fault Diagnosis Theories and Methods

Cited by 13 publications

References 5 publications

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

Detecting the singularities in engineering signals

System health monitoring and prognostics — a review of current paradigms and practices

Contact Info

Product

Resources

About