The automotive industry is undergoing a period of rapid advancement, as OEMs race to develop the next generation of electric and autonomous vehicles. Many manufacturers are investing in prognostics technology, which has made advancements mainly in the aerospace industry over the past couple decades. Unlike aerospace applications, which have relatively more safety-critical systems, it can be more challenging to identify a business case for developing a prognostics or early fault detection system for an automotive application. In the retail setting, early fault detection systems may increase warranty costs, and the benefits to customer satisfaction may not be worth this additional cost. For fleet managers who own and operate many vehicles, however, a business case can be made based on the value of preventing unexpected downtime and unnecessary maintenance. Developing a reliable early fault detection algorithm for a complex system can be an expensive undertaking, requiring many parts, months of data collection, and possibly years of effort, so it is important to understand the possible return on investment for the effort.
In this paper, we present a method to model the business value of an early fault detection system. The method is generic and may be applied to any system where the failure modes are purely fatigue based (i.e. abuse modes are excluded), and the failure rate of each part in the system can be independently modelled using a time-to-failure probability density function. The model is based on Monte Carlo simulation, and the assumptions and limitations are explored. The model can be used to estimate the expected savings from implementing an early fault detection system and derive requirements on the true positive and false positive rates required for the fault detection system to meet its business objectives. An example is presented with application to a two-stage gearbox, such as one that may be found in an electric vehicle powertrain. The example shows how to estimate the parameters for each component, how to estimate the costs associated with failure, and ultimately how to interpret the model outputs and drive business decisions.
Calibration and Monte-Carlo Simulation of the Detection System PTOLEMEOS