Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision system

Goddu, G.; Li, Bo; Chow, Mo-Yuen; Hung, J. C.

doi:10.1109/iecon.1998.724018

Cited by 26 publications

(18 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, techniques based on statistical or geometric methods, neural networks, expert systems, fuzzy and neuro-fuzzy approaches have proven very effective, although often remain "black boxes" as to the interpretation of the physical relationships underpinning the fault classification [2][3][4][5]. In this work, a systematic approach to fault classification is propounded.…”

Section: Introductionmentioning

confidence: 98%

Fuzzy Decision Trees as Intelligent Decision Support Systems for Fault Diagnosis

Zio

Baraldi

Popescu

2008

Intelligent Decision and Policy Making Support Systems

View full text Add to dashboard Cite

Summary. In the present work, an intelligent decision support system is proposed to assist the operators in fault diagnosis tasks. The underlying approach relies on a systematic procedure to manipulate measured data of the monitored variables for constructing transparent fuzzy if-then rules associating different patterns of evolution to different faults and anomalies. The resulting fuzzy classification model can then be represented in the form of a Fuzzy Decision Tree. A case study regarding the classification of simulated faults in the feedwater system of a Boiling Water Reactor is presented.

show abstract

Section: Introductionmentioning

confidence: 98%

Fuzzy Decision Trees as Intelligent Decision Support Systems for Fault Diagnosis

Zio

Baraldi

Popescu

2008

Intelligent Decision and Policy Making Support Systems

View full text Add to dashboard Cite

show abstract

“…Several methods have been used to analyze the vibration signal in order to extract effective features for bearing fault detection. Among them, Heng and Nor [2] used statistical data such as mean value and standard deviation to monitor the bearing conditions; Li et al [3] and Ye et al [4] trained an artificial neural network (ANN) from the frequency and amplitude data of the bearing system; Goddu et al [5] applied fuzzy logic inference rules to judge the bearing conditions; Zarei [1] and Eren [6] used the wavelet packet transform to decompose the vibration signals; Cheng [7] and Yu [8] used the empirical mode decomposition (EMD) and Hilbert spectrum to analyze the experiment data and moved away from the non-stationary mode of bearing faults. Feature extraction is one of the most important factors in pattern recognition problems.…”

Section: Introductionmentioning

confidence: 99%

Application of cepstrum and neural network to bearing fault detection

2009

View full text Add to dashboard Cite

This paper proposes an integrated system for motor bearing diagnosis that combines the cepstrum coefficient method for feature extraction from motor vibration signals and artificial neural network (ANN) models. We divide the motor vibration signal, obtain the corresponding cepstrum coefficients, and classify the motor systems through ANN models. Utilizing the proposed method, one can identify the characteristics hiding inside a vibration signal and classify the signal, as well as diagnose the abnormalities. To evaluate this method, several tests for the normal and abnormal conditions were performed in the laboratory. The results show the effectiveness of cepstrum and ANN in detecting the bearing condition. The proposed method successfully extracted the corresponding feature vectors, distinguished the difference, and classified bearing faults correctly.

show abstract

“…The faults arising in mechanical systems are often linked with bearing faults. In many instances, the accuracy of the instruments and devices used to monitor and control the mechanical system is highly dependent on the dynamic performance of bearings (Goddu 1998).…”

Section: Introductionmentioning

confidence: 99%

Online tribology ball bearing fault detection and identification

Ling

Khonsari

2007

SPIE Proceedings

View full text Add to dashboard Cite

We present a feasibility analysis for the development of an online ball bearing fault detection and identification system. This system can effectively identify various fault stages related to the evolution of friction within the contact in the coated ball bearings. Data are collected from laboratory experiments involving forces, torque and acceleration sensors. To detect the ball bearing faulty stages, we have developed a new bispectrum and entropy analysis methods to capture the faulty transient signals embedded in the measurements. Test results have shown that these methods can detect the small abnormal transient signals associated with the friction evolution. To identify the fault stages, we have further developed a set of stochastic models using hidden Markov model (HMM). Instead of using the discrete sequences, our HMM models can incorporate the feature vectors modeled as Gaussian mixtures. To facilitate online fault identification, we build an HMM model for each fault stage. At each evaluation time, all HMM models are evaluated and the final detection is refined based on individual detections. Test results using laboratory experiment data have shown that our system can identify coated ball bearing faults in near real-time.

show abstract

Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision system

Cited by 26 publications

References 5 publications

Fuzzy Decision Trees as Intelligent Decision Support Systems for Fault Diagnosis

Fuzzy Decision Trees as Intelligent Decision Support Systems for Fault Diagnosis

Application of cepstrum and neural network to bearing fault detection

Online tribology ball bearing fault detection and identification

Contact Info

Product

Resources

About