Condition based maintenance (CBM) aims to reduce maintenance cost and improve equipment reliability by effectively utilizing condition monitoring and prediction information. It is observed that the prediction accuracy often improves with the increase of the age of the component. In this research, we develop a method to quantify the remaining life prediction uncertainty considering the predictio...

Wireless devices and systems have been deployed in many communication sectors but have not yet been adequately adapted to harsh industrial environments. Cost effective, reliable and scalable wireless technologies have yet to be introduced in industrial plants, including petroleum plants. At this time, there are very few, if any, cost-effective Commercial-Off-The-Shelf (COTS) radios available th...

In this paper we discuss interesting developments of expert systems for machine diagnosis and condition-based maintenance. We review some elements of condition-based maintenance and its applications, expert systems for machine diagnosis, and an example of machine diagnosis. In the last section we note some problems to be resolved so that expert systems for machine diagnosis may gain wider accep...

This paper presents a bearing run-to-failure data simulation model for condition based maintenance, in which a single point outrace fault can be implemented. According to the characteristic that bearings life follows the Weibull distribution, amount of bearing run-to-failure datasets can be simulated which can be used to verify the feature extraction, remaining useful life prediction and mainte...

The high costs in maintaining today’s complex and sophisticated equipment make it necessary to enhance modern maintenance management systems. Conventional condition-based maintenance (CBM) reduces the uncertainty of maintenance according to the needs indicated by the equipment condition. The intelligent predictive decision support system (IPDSS) for condition-based maintenance (CBM) supplements...

Abstract: A condition based preventive maintenance (CBPM) policy is proposed for a continuously operating device whose condition deteriorates with time in service. The model incorporates both deterioration and random failures. Deterioration is modeled as discrete states process. The system undergoes random inspections to know the condition, mean time between inspections are exponentially distri...

Condition-based maintenance has been proven effective in reducing unexpected failures with minimum operational costs. This study considers an optimal condition-based replacement policy with optimal inspection interval when the degradation conforms to an inverse Gaussian process with random effects. The random effects parameter is used to account for heterogeneities commonly observed among a pro...

The semi-Markov decision model is a powerful tool in analyzing sequential decision processes with random decision epochs. In this paper, we have built the semi-Markov decision process (SMDP) for the maintenance policy optimization of condition-based preventive maintenance problems, and have presented the approach for joint optimization of inspection rate and maintenance policy. Through numerica...

Emergence of uniquely addressable embeddable devices has raised the bar on Telematics capabilities. Though the technology itself is not new, its application has been quite limited until now. Sensor based telematics technologies generate volumes of data that are orders of magnitude larger than what operators have dealt with previously. Real-time big data computation capabilities have opened the ...

Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the d...