Development progress

• optimisation models (TODO)

  • not started

• weibull estimation

  • 2p complete

    • rank regression (NA)

    • MLE

      • graph

      • confidence interval (hessian, 1 and 2 side)

  • 2p censored

    • rank regression

      • graph

      • confidence interval (median rank)

    • MLE

      • graph

      • confidence interval (hessian, 1 and 2 side)

  • auto selection of fit method

• 3p complete

  • rank regression.

  • MLE (todo)

Description

Utilisation of information collected by CMMS (computerised maintenance management systems) for improving a maintenance policy is a long-standing problem. In many organisations failure and cost data from their asset is not used at all. This tool will automatically choose most suitable maintenance policy by solving optimisation problems.

Optimization problems

• Component replacement interval

• Inspection interval

• Capital equipment replacement interval

• Maintenance resource requirements

Parameters:

• Cost.

  • Maintenance cost

  • Operation cost

  • Replacement cost

• Failure rate. WEMIOT has its own module for evaluation parameters of Weibull distribution based on failure information from CMMS.

Constraints

• Avialability

• Safety

• other

References

1. Labib, Ashraf W. "A decision analysis model for maintenance policy selection using a CMMS." Journal of Quality in Maintenance Engineering (2004).

2. Jardine, Andrew KS, and Albert HC Tsang. Maintenance, replacement, and reliability: theory and applications. CRC press, 2013.

3. Aggarwal, Charu C. Linear Algebra and Optimization for Machine Learning: A Textbook. Springer Nature, 2020.