Predictive maintenance using FMECA method and NHPP models

Nishit Kumar Srivastava, Sandeep Mondal
2014 International Journal of Services and Operations Management  
Most of predictive maintenance technologies are inaccessible to small scale and medium scale industries due to their demanding cost. This paper proposes a predictive maintenance policy using failure mode effect and criticality analysis (FMECA) and non-homogeneous Poisson process (NHPP) models which require minimal use of advanced monitoring technologies and sophisticated data acquisition systems. Most of the repairable systems show long term reliability degradation with repeated overhauls.
more » ... critical component of a system or machinery exhibiting sad (deteriorating) trend is used as an indicator to predict overall maintenance time of a system. Firstly, the component to be used as an indicator for predictive maintenance is chosen using FMECA method, in which the most critical component is chosen. Secondly, the failure data of the chosen component is analysed using NHPP models and based on analysis of the data, relevant NHPP model is selected and finally, the Mean Time Between Failure (MTBF) of the component is compared with the threshold mean time between failure [MTBF(Th)] of the component to decide the overall maintenance time for the system. The developed methodology is validated on an overhead crane in a steel manufacturing company. He has done BTech (Bachelor of Technology) in Electrical Engineering and MTech (Master of Technology) in Industrial Engineering and Management from Indian School of Mines, Dhanbad. His areas of interest are manufacturing management, plant and machine maintenance, productivity improvement in service and manufacturing sectors and operations management. Sandeep Mondal is an Associate Professor at Indian School of Mines, Dhanbad. He completed his Master degree from Indian Statistical Institute, Kolkata and his PhD in Remanufacturing from Indian School of Mines, Dhanbad. He has done several projects funded by UGC and MHRD. His areas of interest are reverse logistics, remanufacturing and empirical data analysis.
doi:10.1504/ijsom.2014.065367 fatcat:mp2xi6obbjbg7dututr3a2qhry