Monitoring and Control of Faults in the Automation Industry Using Soft Sensors and Ant Colony Optimization Fourier Transform Perceptron with Multiple Layers

Abstract

Abstract In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. Once soft sensors are designed, a mechanism to maintain or update these models is highly desirable in industry. This paper proposed novel technique in monitoring and control optimization of soft sensors in automation industry for fault detection. Here fault detection has been carried out using Probabilistic Multi-Layer Fourier Transform Perceptron (PMLFTP). The input collected plant data has been pre-processed for removal of samples containing null values.Then fault detection and diagnosis process have been carried out based on probability of the data with Fourier transform-based detection and multi-layer perceptron-based diagnosis of the fault in the manufacturing process. Then controlling of data in soft sensors has been optimized using auto-regression based ant colony optimization (AR_ACO) which has effect in increasing the production of industry automatically. The experimental results have been carried out in terms of computational rate of 40%, QoS of 78%, RMSE of 45%, fault detection rate of 90%, control optimization of 93% has been obtained for various historical data-based evaluations.