Prediction of wear trend of engines via on-line wear debris monitoring

Cao, Wei; Dong, Guangneng; Xie, Youbai; Peng, Zhongxiao

doi:10.1016/j.triboint.2018.01.015

Cited by 78 publications

(41 citation statements)

References 21 publications

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“…Wear state of machine components in operation is detected and on-line monitored by using single or multiple techniques of vibration measurement, oil analysis, acoustic emission, image analysis, and proximity probes, etc. An on-line visual ferrograph (OLVF) method was used to monitor the wear of a small-scale four-cylinder diesel engine for 400 h operation [244]. The images of wear debris in lubricant oil were continuously captured, and the image features were extracted after data correction, reconstruction, and de-noising.…”

Section: Wear Monitoring and Debris Analysismentioning

confidence: 99%

A review of recent advances in tribology

et al. 2020

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The reach of tribology has expanded in diverse fields and tribology related research activities have seen immense growth during the last decade. This review takes stock of the recent advances in research pertaining to different aspects of tribology within the last 2 to 3 years. Different aspects of tribology that have been reviewed including lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology. This review attempts to highlight recent research and also presents future outlook pertaining to these aspects. It may however be noted that there are limitations of this review. One of the most important of these is that tribology being a highly multidisciplinary field, the research results are widely spread across various disciplines and there can be omissions because of this. Secondly, the topics dealt with in the field of tribology include only some of the salient topics (such as lubrication, wear, surface engineering, biotribology, high temperature tribology, and computational tribology) but there are many more aspects of tribology that have not been covered in this review. Despite these limitations it is hoped that such a review will bring the most recent salient research in focus and will be beneficial for the growing community of tribology researchers.

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Section: Wear Monitoring and Debris Analysismentioning

confidence: 99%

A review of recent advances in tribology

et al. 2020

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“…oil replenishment, oil loss, oil filter, and operating conditions affect the WDC and are observable in the form of noise and/or anomalous spikes in the degradation trend. The factors affecting WDC have been considered for wear prediction using the relevance vector machine method by Cao et al [11]. Fan et al [12] discuss the mapping of WDC under different machine wear rates.…”

Section: Introductionmentioning

confidence: 99%

Lubricating Oil Remaining Useful Life Prediction Using Multi-Output Gaussian Process Regression

Tanwar

Raghavan

2020

IEEE Access

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Lubricant condition monitoring (LCM) is a preferred condition monitoring (CM) technology for fault diagnosis and prognosis owing to its ability to derive a wide range of information from the system (machine/equipment) state and lubricant state. Given the importance of LCM for maintenance decision support, an accurate and reliable remaining useful life (RUL) prediction framework is necessary. The LCM health information in the form of degradation trends is therefore evaluated using numerous statistical, model-based, and artificial intelligence approaches by various researchers. A multitude of factors widely affects the degradation trends viz. operating conditions, environmental variations, oil replenishments, oil loss, chemical breakdown, etc. These factors increase the complexity of the time-series degradation trends making RUL prediction intractable using several of the standard statistical approaches. Therefore, limited research is available on lubricating oil RUL prediction with these influential factors accounted for. Focusing on the complexity of the degradation trend with oil replenishment effects (ORE), we propose the use of the Gaussian process regression (GPR) model for RUL prediction in this study. The model has an advantage over other data-driven approaches as it is a non-parametric Bayesian method. To exploit prior information and historical data collected, the approach is extended to multi-output GPR (MO-GPR) which effectively defines the correlations between historical degradation trends for similar lubrication systems with the current degradation pattern of a system being monitored in real-time. Three different oil replenishment strategies are considered under MO-GPR to demonstrate the applicability and flexibility of this method.

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“…Technical services are a need that can cause breaks in the use of the machine [11,12]. Delaying service activities, disregarding the recommendations of the manufacturer of the machine, and replacing exploitative materials intended for exchange with another substitute (often of dubious quality) can result in undesirable failure of the machine [13,14]. Preventive service actions are the most important service activities of machines.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support Method for Choosing an Agricultural Machinery Service Workshop Based on Fuzzy Logic

Osuch

Rybacki

et al. 2020

Agriculture

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The quality of technical services is one of the main criteria for assessing the service processes of agricultural machinery, and it has a significant impact on the decision-making process when choosing a service provider. Technical service quality has a significant role in maintaining agricultural machinery in optimal technical condition, thus ensuring its high reliability and durability. The purpose of this study is to present a decision support method for choosing the right agricultural machinery service facility. The method is based on fuzzy inference. The choice of service workshop is based on decision criteria individually accepted by farmers (experts). The method was checked by way of research carried out among 25 farmers facing the choice of a service facility. The decision-making process allows for ranking the decision criteria and decision-makers. The results of the presented research can be used by farm owners and service companies to plan their development directions.

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Prediction of wear trend of engines via on-line wear debris monitoring

Cited by 78 publications

References 21 publications

A review of recent advances in tribology

A review of recent advances in tribology

Lubricating Oil Remaining Useful Life Prediction Using Multi-Output Gaussian Process Regression

A Decision Support Method for Choosing an Agricultural Machinery Service Workshop Based on Fuzzy Logic

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