Abstract
Troubleshooting is the process of diagnosing and repairing a system that is behaving abnormally. It involves performing various diagnostic and repair actions. Performing these actions may incur costs, and traditional troubleshooting algorithms aim to minimize the costs incurred until the system is fixed. Prognosis deals with predicting future failures. We propose to incorporate prognosis and diagnosis techniques to solve troubleshooting problems. This integration enables (1) better fault isolation and (2) more intelligent decision making with respect to the repair actions to employ to minimize troubleshooting costs over time. In particular, we consider an anticipatory troubleshooting challenge in which we aim to minimize the costs incurred to fix the system over time, while reasoning about both current and future failures. Anticipatory troubleshooting raises two main dilemmas: the fix–replace dilemma and the replace-healthy dilemma. The fix–replace dilemma is the question of how to repair a faulty component: fixing it or replacing it with a new one. The replace-healthy dilemma is the question of whether a healthy component should be replaced with a new one in order to prevent it from failing in the future. We propose to solve these dilemmas by modeling them as a Markov decision problem and reasoning about future failures using techniques from the survival analysis literature. The resulting algorithm was evaluated experimentally, showing that the proposed anticipatory troubleshooting algorithms yield lower overall costs compared to troubleshooting algorithms that do not reason about future faults.
Highlights
Maintenance activities in industrial systems are important in order to reduce product or system life-cycle costs
We show that the Markov decision process (MDP) approach significantly reduces troubleshooting costs
In the first set of experiments, we focused on the fix–replace dilemma, and we compared the baselines always-repair (AR) and always-fix (AF) with DR1 and NMDPFixRep
Summary
Maintenance activities in industrial systems are important in order to reduce product or system life-cycle costs. Corrective maintenance is an important type of maintenance activity that involves troubleshooting observed failures. Troubleshooting a failure involves identifying its root cause and performing repair actions in order to return the system to a healthy state. We propose a novel troubleshooting algorithm that reduces troubleshooting costs by using an integration of diagnosis and prognosis techniques. Diagnosis deals with understanding what has happened in the past that has caused an observed failure. We consider prognosis techniques that generate survival curves of components. These are curves that plot the likelihood of a component to survive (i.e., not fail) as a function of the components usage or age [1]. There are analytical methods for creating survival curves and sophisticated machine learning techniques that can learn survival curves from data [2]
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