Improved Backpropagation Algorithm for Diesel Engine Fault Diagnosis with MATLAB Implementation

This article provides an in-depth exploration of the MATLAB implementation for an improved backpropagation algorithm designed for diesel engine fault diagnosis. The program employs an enhanced neural network architecture with modified weight update mechanisms and optimized activation functions to achieve superior fault detection accuracy and diagnostic reliability. Engineering students particularly benefit from studying this implementation as it demonstrates practical applications of neural networks in mechanical system diagnostics, showcasing how backpropagation algorithms can be adapted for real-world engineering problems. The program structure includes data preprocessing modules, network initialization routines, and training loops with convergence monitoring. We will detail the algorithmic enhancements such as adaptive learning rates and momentum optimization that prevent local minima trapping, along with the implementation of cross-validation techniques for performance verification. The code organization follows MATLAB's neural network toolbox conventions while incorporating custom functions for feature extraction and result visualization, enabling users to effectively analyze diesel engine operational patterns and identify potential failures.