Abstract
Risk-based maintenance (RBM) aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the optimal maintenance time with minimal cost and safety constraints. The dynamic evolution model provides qualified risks by using probabilistic inference with bucket elimination and gives the prospective degradation trend of a complex system. Based on the degradation trend, an optimal maintenance time can be determined by minimizing the expected maintenance cost per time unit. The effectiveness of the proposed method is validated and demonstrated by a collision accident of high-speed trains with obstacles in the presence of safety and cost constrains.
Highlights
Safety-critical systems, such as chemical factory, nuclear plant, and train control systems, are those where failures could result in loss of life, significant property damage, or damage to the environment
As condition-based maintenance (CBM) systems have been implemented in a way to continuously output data that is calculated against the status and performance of the equipment, the decision making in CBM focuses on predictive maintenance (PdM) which promises to reduce downtime, spare inventory, maintenance cost, and safety hazards
It consists of the following steps: (1) identification of components, subsystems, system, and their relationships: the system is divided into subsystems, and the components of each subsystem and their relationships are identified; we model the system structure by using a special case of dynamic Bayesian network, the 2-slice temporal Bayesian network (2-TBN); (2) Collecting failure data, failure model and failure rate: the information is encoded in the conditional probability table (CPT) in 2-TBNg based maintenance model.(3) Risk assessment and evaluation: by using probabilistic inference with bucket elimination, a consequence analysis is implemented to quantify the effect of the occurrence of each failure scenario and obtain quantitative measure for its associated risks
Summary
Safety-critical systems, such as chemical factory, nuclear plant, and train control systems, are those where failures could result in loss of life, significant property damage, or damage to the environment. Comparing with the complex junction-tree based inference, an attractive property of bucket elimination approaches is that it is relatively easy to understand and implement. It consists of the following steps: (1) identification of components, subsystems, system, and their relationships: the system is divided into subsystems, and the components of each subsystem and their relationships are identified; we model the system structure by using a special case of dynamic Bayesian network, the 2-TBN; (2) Collecting failure data, failure model and failure rate: the information is encoded in the CPT in 2-TBNg based maintenance model.(3) Risk assessment and evaluation: by using probabilistic inference with bucket elimination, a consequence analysis is implemented to quantify the effect of the occurrence of each failure scenario and obtain quantitative measure for its associated risks. The risk is used to study maintenance costs including the costs incurred as a result of failure. (4) Optimal maintenance strategy: by defining different maintenance costs, the optimal maintenance scheme can be derived by applying the optimization theory to the risk quantitative measure computed in the aforementioned step
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