Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance

By Gang Niu, Bo-Suk Yang, Michael G. Pecht. 2010

273 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “Thus, preventive maintenance has been studied in [124], [125], [175], [176] and attempts to predict the remaining useful life (RUL) of a machine accurately.”
Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain (2016, background): “This topic has attracted the attention of many researchers over the last few years [1, 2], emphasizing the necessity of health monitoring and maintenance procedures in manufacturing operations in order to ensure equipment availability, product quality, ontime deliveries, and plant safety [3–6].”
Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life (2011, background): “The only relevant work we are aware of comes from [37] where only two data-driven algorithms are employed as member algorithms and their weights are empirically determined based on a one-time training error without a systematic scheme for error estimation and performance validation.”
Cloud-enabled prognosis for manufacturing (2015, methodology): “Niu et al. [247] proposed a condition-based maintenance system that employed a reliability-centered maintenance mechanism to optimize maintenance cost, as shown in Fig.”
An overview of time-based and condition-based maintenance in industrial application (2012, methodology): “Niu, Yang, and Pecht (2010) developed a novel condition-based maintenance system that used a reliability-centred maintenance (RCM) mechanism to optimise maintenance costs, and employed a data fusion strategy for improving condition monitoring, health assessment, and prognostics.”
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics (2017, methodology): “[25] presented a CBM system that uses reliability-centered maintenance mechanism to optimize main-”