Condition-based maintenance (CBM) and predictive maintenance (PDM) are technically and economically attractive goals in the context of digitization. In reality, however, there are considerable hurdles to overcome on the way to preventive maintenance. The following success criteria should be observed:
Preventive maintenance makes economic sense if high entailed costs of failures are prevented. For the business case “maintenance optimization”, not only direct costs from downtimes and repair expenses must be considered, but also indirect costs resulting, for example, from delays in commuter traffic. This consideration results in a CBM/PDM potential for capital-intensive, networked industrial plants, for stand-alone plants such as wind farms, for seafaring and for public transport.
The focus on high-risk and costly components keeps the scope of tasks for preventive processes compact. This enables the rapid implementation of the concrete goals with RoI potential to process maturity. The focus also results in a correspondingly compact – thus feasible – requirement for information procurement and the data process.
Meanwhile, there is generally monitoring data of plants and vehicles. However, their quality must be ensured if reliable recommendations are to be derived from them. Because data is often stored on disconnected IT systems, the interoperability of these sources must also be realized.Operationally, there must be flexibility for event-based expenditures in the planned maintenance process. Finally, the practical and legal limitations of the solution space need to be examined.
The path to success in 3 steps:
A careful requirement analysis delivers the priority targets according to the criteria of the respective industry. For public transport this would be the increase in reliability; e.g. measured as Mean Distance Between Failure (MDBF). For off-shore wind turbines the greatest savings result from preventive parts replacement based on automated diagnostics. Along with the technical tasks, the necessary adjustments to the maintenance process are worked out with those responsible. Here, too, the industry-specific framework conditions and regulations must be observed.
The currently often rudimentary data process is put on a solid organizational and technical basis, implemented, tested and rolled out. The availability and quality assurance of the data are the central factors. Uptime HARVEST is the software for preventive maintenance. It provides reliable recommendations and warnings, which is crucial for acceptance in the change process. The software supports and guides the process from risk analysis to monitoring for a recommendation system to operational implementation in the Computerized Maintenance System (CMMS).
CBM/PDM transfers the maintenance process from reactive to preventive. The time-based, planned process is supplemented by event-based measures. The maintenance quality is ensured by the ongoing monitoring of the systems or vehicles. In the preventive process the predictability will become even higher than today, because of decreasing repair rates and the prewarning of failure risks. The difficult transition phase to preventive maintenance becomes a success if the process design is developed and implemented together with the maintenance team. This change process is developed by the Uptime Engineering consultants together with the customer’s technicians.
