Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

Abstract

The key to reducing operation and maintenance costs and improving reliability is to evaluate the condition and fault detection methods of ship ballast water systems. To reduce the impact of support vector machine (SVM) parameter uncertainty and improve the accuracy of ship ballast water system fault diagnosis models, this paper proposes a multi-strategy collaborative improved sparrow search algorithm (ISSA) to optimize the parameters of SVM for fault diagnosis. First, ISSA initializes the population using tent chaotic maps to improve spatial distribution uniformity and stabilize the population quality. Second, the discoverer in the algorithm adopts an adaptive weighting strategy to avoid falling into local optima and accelerate the convergence speed. Finally, the follower position introduces mutation strategies and chaotic perturbations to enhance local evolution capabilities and improve the utilization of search areas. The experimental results show that compared with sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), Genetic Algorithm (GA), whale optimization algorithm (WOA), and grey wolf optimization algorithm (GWO), ISSA can obtain better optimal solutions at the fastest convergence rate on the benchmark test function. On the dataset of the ship ballast water system, the ISSA-SVM fault diagnosis model proposed in this paper has the best classification performance, with an average accuracy of 97.44%. Compared with the other two models, ISSA-SVM is 7.55% and 0.83% higher than SVM's 89.89% and SSA-SVM's 96.61%.

Rotating Machinery Fault Diagnosis with Limited Multisensor Fusion Samples by Fused Attention-Guided Wasserstein GAN

“…In addition, model-based methods require complex parameter adjustment and correction, which is difficult to satisfy for cases with real-time requirements. In contrast, traditional data-driven machine learning algorithms, such as support vector machines [5], extreme learning machines [6] and random forests [7], are simpler and more effective at fitting data. Nevertheless, due to their shallow model structure, these algorithms struggle to extract deeper features from the data.…”