Practical prognostics for Condition Based Maintenance

This paper describes the results of the author's research into the architecture of the infrastructure required to implement prognostics in a Condition Based Maintenance (CBM) environment for the UK MoD and others. Prognostics technology is being developed for a wide range of platforms to provide their maintenance support and logistics networks a forecast of maintenance demand based on the condition of the platform's equipment. The impact of prognostics technology in the management of CBM, or Directed Logistics/Performance Based Logistics is discussed. There are two conflicting requirements for prognostics in CBM. The first is in strategic forecasting to provide a better scaling of the network to assist in the leaning of the processes to make the network more efficient. Lean networks are necessarily less agile as with less free resource they are unable to react to changes in demand in a timely manner. It is in improving agility that prognostics provide the largest opportunity to improve Maintenance Repair & Overhaul (MRO) network performance. Being able to improve both the leanness whilst simultaneously improving its agility of the network prognostics sets a new paradigm in logistics management. In particular the impact of prognostics on the Lean-Agile dilemma is described with a practical example. The differing components of a platforms prognostic capability are analyzed. These include reliability data, usage/abusage data and health data. These three types of data need to be integrated to provide a uniform prediction of the platforms future airworthiness. Similarly the technology for integration of prognostics with logistics agents is necessary to forecast the impact of changes identified by the platforms prognostics on the MRO logistics network. The paper describes a unified method of achieving this integration based on Discrete Event Simulation techniques. Finally the constraints on the architecture for any resulting prognostics based Maintenance Management Enterprise Resource Planning (ERP) system are discussed. Near and far horizon forecasts place differing demands and constraints on the ERP systems design and the data acquisition systems that serve the system.