Predictive reliability mining for early warnings in populations of connected machines

Abstract

Traditional reliability analysis of complex machinery involves statistical modeling of historical data on part failures from warranty claims, using distributions from exponential family such as the Weibull or log-normal distribution. When observed failures (in one or more parts) across a population of machines exceed the number expected based on such a model, this may serve as an early warning of a potential systemic problem with the population. Of course, such early warnings rely on some exceptionally high failures having actually occurred. However, modern connected vehicles, engines and machines of all kinds are equipped with on-board electronics that transmit alerts, referred to as `diagnostic trouble codes' or DTCs over the network, whenever abnormal conditions are detected. Such DTC signals should also be able to serve as early-warning indicators, typically before actual failures are observed in large numbers. In this paper, we develop a graphical Bayesian model that augments standard reliability analysis with early-warning indicators such as DTC signals observed over the industrial Internet. We demonstrate that our augmented model can detect of potential problems earlier than that using traditional reliability analysis. Going further, we note that significant deviations from expected failure counts might often occur only in some unknown subset of the population, e.g., a particular batch, or machines manufactured at a particular plant. In such cases, deviations from expected numbers are insignificant across the full population. We present a rule mining technique that discovers such subsets efficiently even when the number of dimensions across which a subset may be defined is large. We term our approach as reliability mining since it combines the use of a Bayesian reliability model with subgroup discovery using data mining techniques. We present experimental results using synthetically simulated scenarios as well as real-life data from a major global automobile manufacturer.