A prognostic algorithm for machine performance assessment and its application

Yan, Jihong; Koç, Muammer; Lee, Jay

doi:10.1080/09537280412331309208

Cited by 205 publications

(100 citation statements)

References 4 publications

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“…• minimization of machines downtime and better productivity [23]; • moving from fail and fix strategy to predict and prevent [9]; • reduction of inventory due to the knowledge beforehand about the time to failure; this knowledge allows planners to order only the needed spare parts when required; • total life cycle cost management optimization due to improvement of CBM using prognostics [24]; • unseen degradation detected and projected from systemmonitored parameters. Figure 5 shows some benefits of diagnostics and prognostics.…”

Section: Prognosticsmentioning

confidence: 99%

See 1 more Smart Citation

Prognostics: a literature review

Elattar

Elminir

Riad

2016

Complex Intell. Syst.

147

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Integrated systems health management (ISHM) is an enabling technology used to preserve safe and reliable operation of complex engineering systems. It also helps in reducing processing and operation time, manpower and cost, and increasing system availability and utility. ISHM includes various technologies ranging from design, analysis, build, and verify to operate and maintain. Prognostics is one of the most challenging and beneficial aspects of ISHM. Knowledge of the remaining useful life using prognostics can make a significant paradigm shift in ISHM. Researchers that have new interest in prognostics need to read hundreds of articles to have a complete picture about prognostics and its relation to other disciplines. Our contribution to solving this problem is by introducing the first comprehensive vision about prognostics as a part of ISHM in a single literature review paper. We focus on prognostics benefits, approaches, applications, and challenges. This paper can be considered as the starting point for studying prognostics and health management.

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Section: Prognosticsmentioning

confidence: 99%

“…Yan et al [23] developed an online prognostics algorithm for machine performance assessment to monitor system health and predict future failure. This allows proactive maintenance in various industries such as an elevator door motion.…”

Section: Industrial Applicationsmentioning

confidence: 99%

Prognostics: a literature review

Elattar

Elminir

Riad

2016

Complex Intell. Syst.

147

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show abstract

“…This is impossible in case of newly installed machine in normal operating condition. Yan et al [15] proposed a solution for this problem by using technician's experience for acceptable level or unacceptable level to predefine the probability thresholds. However, these levels are non-scientific and human intuitiveness.…”

Section: Introductionmentioning

confidence: 99%

“…In case of using model-based techniques, proportional hazard model (PHM) and logistic regression model (LRM) were applied for performance machine reliability indices and machine RUL prediction [13]. Other applications of LRM could be found in references [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Machine Performance Degradation Assessment and Remaining Useful Life Prediction Using Proportional Hazard Model and SVM

Tran

Pham

Yang

et al. 2012

Engineering Asset Management and Infrastructure Sustainability

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This paper proposes a three-stage method involved system identification techniques, proportional hazard model, and support vector machine for assessing the machine health degradation and forecasting the machine remaining useful life (RUL). In the first stage, only the normal operating condition of machine is used to create identification model to mimic the dynamic system behaviour. The machine degradation is indicated by degradation index which is the root mean square of residual errors. These errors are the difference between identification model and behaviour of system. In the second stage, the Cox's proportional hazard model is generated to estimate the survival function of the system. Finally, support vector machine, one of the remarkable machine learning techniques, in association with direct prediction method of time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine are used for appraising the proposed method. The results indicate that the proposed method could be used as a potential tool to machine prognostics.

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“…Recently, an increasing number of scholars have studied different methods on bearing performance degradation assessment and most of them have made certain achievements. For example, Yan and co-workers [1,2] realize machine performance assessment based on logistic regression. Baydar and Ball [3] analyses the time-frequency diagram of different tooth wear degree under different load based on IPS(Instantaneous Power Spectrum), which found IPS is able to distinguish different fault degree.…”

Section: Introductionmentioning

confidence: 99%

Assessment Method for Rolling Bearing Performance Degradation Using TESPAR and GMM

Zhang¹,

Huang²,

Xiong³

et al. 2014

TOMEJ

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Rolling bearing performance degradation assessment has been receiving much attention for which itscrucial role to realize CBM(condition-based maintenance).This paper proposed a novel bearing performance degradation method based on TESPAR(Time Encoded Signal Processing and Recognition)and GMM(Gauss Mixture Model). TESPAR is used to extracted features which constitute A-matrix. GMM is utilized to approximate the density distribution of singular values decomposed by A-matrix. TENLLP(Time-Encoded Negative Log Likelihood Probability) serves as a fault severity which can display the similarity of the singular values between normal samples and fault samples as quantificational. Results of its application to bearing fatigue test show that this performance degradation assessment can detect the incipient rolling bearing fault and be sensitive to the change of fault.

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A prognostic algorithm for machine performance assessment and its application

Cited by 205 publications

References 4 publications

Prognostics: a literature review

Prognostics: a literature review

Machine Performance Degradation Assessment and Remaining Useful Life Prediction Using Proportional Hazard Model and SVM

Assessment Method for Rolling Bearing Performance Degradation Using TESPAR and GMM

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