Forecasting Components Failure Using Ant Colony Optimization For Predictive Maintenance
Abstract: Failures are the eminent aspect of any machine and so is true for vehicle as it is one of the sophisticated machines of today’s time. Early detection of faults and prioritized maintenance is a necessity of vehicle manufactures as it enables them to reduce maintenance cost and increase customer satisfaction. In our research, we have proposed a method for processing Logged Vehicle Data (LVD) that uses Ant-Miner algorithm which is a Ant Colony Optimization (ACO) based Algorithm. It also utilizes processes like Feature engineering, Data preprocessing. We tried to explore the effectiveness of ACO for solving classification problem in the form of fault detection and prediction of failures which would be used for predictive maintenance by manufacturers. From the seasonal and yearly model that we have created, we have used ACO to successfully predict the time of failure which is the month with highest likelihood of failure in vehicle’s components. Here, we also validated the obtained results. LVD suffers from data imbalance problem and we have implemented balancing techniques to eliminate this issue, however more effective balancing techniques along with feature engineering is required to increase accuracy in prediction.
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