Real-time abnormality detection and classification in diesel engine operations with convolutional neural network

Abstract

In this paper, we propose a real-time diagnostic method using a convolutional neural network (CNN) to detect cylinder misfires and engine load conditions in multi-cylinder internal combustion (IC) diesel engines. To enhance engine efficiency and reliability, it is necessary to detect and classify multiple irregularities in engine operation, such as misfires and changes in load conditions. Optimized combustion parameter setting increases engine efficiency, which depends on the engine load conditions. Similarly, misfire detection prevents engine damage and increases fuel efficiency and reliability of the engine. Previous works investigated misfire detection while ignored the change in engine operating conditions that affects the engine efficiency. This work presents a real-time CNN-based method to detect misfires and load changes together in engine operations by analyzing the IC process. First, the sensor signal is pre-processed to extract a primary signal and translate it into a crank angle degree (CAD) signal, which represents a complete cycle of the IC process in the engine. After that, a CNN is designed for multi-class classification and trained using one-dimensional CAD vectors, which combines a feature extraction capability and pattern recognition in a single learner. The proposed CNN-based method detects and classifies multiple abnormalities in engine operations with an accuracy of more than 99% and low generalization error. The designed CNN uses a single convolutional layer for feature extraction resulting in more efficient systems in terms of performance and speed.