An Immune Detector-Based Method for the Diagnosis of Compound Faults in a Petrochemical Plant

Aiming at the serious overlap of traditional dimensionless indices in the diagnosis of compound faults in petrochemical plants, we use genetic programming to construct optimal indices for that purpose. In order to solve the problem of losing some useful fault feature information due to classification processing, during the generation of the dimensionless index immune detector, such as reduction and clustering, we propose an integrated diagnosis method using each dimensionless index immune detector. Simulation results show that this method has high diagnostic accuracy

A Dimensionless Immune Intelligent Fault Diagnosis System for Rotating Machinery

Aiming at the shortcomings of the traditional frequency domain analysis method, such as failure to find early faults, the misjudgement and omission of fault types, and failure to diagnose complex faults, a new approach is developed, which is different from the existing technical route in the field of fault diagnosis, by closely following real-time online, intelligent and accurate requirements in the field of monitoring and fault diagnosis of large rotating machinery. Combining immune mechanism, dimensionless index, support vector machine and other artificial intelligence technologies, linked with the particularity of fault diagnosis problems, a fault diagnosis classification algorithm based on memory sequence is proposed, and an intelligent fault diagnosis system based on a dimensionless immune detector and support vector machine was developed. Finally, the system was applied to a compressor unit in a petrochemical enterprise and good results were achieved

Digital twin based reference architecture for petrochemical monitoring and fault diagnosis

The whole process of the petrochemical industry involves flammable and explosive dangerous goods. The timely discovery of abnormalities or failures in the petrochemical process is crucial to ensure production safety. This paper sets up the approach to build the Digital Twin System (DTs) of a petrochemical process. Specifically, we decompose the petrochemical process into five levels one by one and build a digital twin plug-in for each component of the component layers, and then inversely decouple the process to assemble the DTs layer by layer. As a specific experimental example, the characteristic DTs is proposed to build modules of temperature field and pressure field and flow field, these DT modules are driven by practical industrial sampling data from cracking furnace, and three characteristic DTS modules stated above are integrated to form DTS. Based on the digital twin technology and DTs, we propose the logical structure of chemical process status monitoring and fault diagnosis in detail, which improves the safety and controllability of the petrochemical process