Corrective maintenance is a maintenance task performed to identify and rectify the cause
failures for a failed system. The engineering equipment gets many components and failure
modes, and its failure mechanism is very complicated. Failure of system-level might occur
due to failure(s) of any subsystem/component. Thus, the symptom failure of equipment
may be caused by multilevel causality of latent failures.
This paper proposes a complete corrective maintenance scheme for engineering equipment.
Firstly, the FMECA is extended to organize the numerous failure modes. Secondly,
the failure propagation model (FPM) is presented to depict the cause-effect relationship
between failures. Multiple FPMs will make up the failure propagation graph (FPG). For a
specific symptom failure, the FPG is built by iteratively searching the cause failures with
FPM. Moreover, when some failure in the FPG is newly ascertained to occur (or not), the
FPG needs to be adjusted. The FPG updating process is proposed to accomplish the adjustment
of FPG under newly ascertained failure. Then, the probability of the cause failures is
calculated by the fault diagnosis process. Thirdly, the conventional corrective maintenance
recommends that the failure with the largest probability should be ascertained firstly.
However, the proposed approach considers not only the probability but also the failure
detectability and severity. The term REN is introduced to measure the risk of the failure.
Then, a binary decision tree is trained based on REN reduction to determine the failure
ascertainment order. Finally, a case is presented to implement the proposed approach on
the ram feed subsystem of a boring machine tool. The result proves the validity and practicability
of the proposed method for corrective maintenance of engineering equipment
Corrective maintenance is a maintenance task performed to identify and rectify the cause
failures for a failed system. The engineering equipment gets many components and failure
modes, and its failure mechanism is very complicated. Failure of system-level might occur
due to failure(s) of any subsystem/component. Thus, the symptom failure of equipment
may be caused by multilevel causality of latent failures.
This paper proposes a complete corrective maintenance scheme for engineering equipment.
Firstly, the FMECA is extended to organize the numerous failure modes. Secondly,
the failure propagation model (FPM) is presented to depict the cause-effect relationship
between failures. Multiple FPMs will make up the failure propagation graph (FPG). For a
specific symptom failure, the FPG is built by iteratively searching the cause failures with
FPM. Moreover, when some failure in the FPG is newly ascertained to occur (or not), the
FPG needs to be adjusted. The FPG updating process is proposed to accomplish the adjustment
of FPG under newly ascertained failure. Then, the probability of the cause failures is
calculated by the fault diagnosis process. Thirdly, the conventional corrective maintenance
recommends that the failure with the largest probability should be ascertained firstly.
However, the proposed approach considers not only the probability but also the failure
detectability and severity. The term REN is introduced to measure the risk of the failure.
Then, a binary decision tree is trained based on REN reduction to determine the failure
ascertainment order. Finally, a case is presented to implement the proposed approach on
the ram feed subsystem of a boring machine tool. The result proves the validity and practicability
of the proposed method for corrective maintenance of engineering equipment
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