A Novel Chaotic Quantum Annealing Algorithm for Optimizing the Target Coverage of Industrial Machinery Fault Monitoring Network

Industrial machinery fault monitoring network (IMFMN) is important technological applications in the industrial field, as they enable real-time monitoring and acquisition of status information for industrial machinery. However, in complex industrial environments, the deployment of sensor nodes poses challenges to network coverage. In order to address the issue of regional coverage in IMFMN and improve network coverage, this paper proposes a new chaotic quantum annealing algorithm (CQA). A new chaotic operator and quantum annealing operator are designed to optimize the deployment of sensor nodes. The chaotic operators introduce randomness to the algorithm, avoiding local optima and improving the global search capability. The quantum annealing operators refresh the evolutionary search through quantum bit representation and quantum rotation gates. They can select predefined candidate solutions among the candidate states and search for the globally optimal predicted solution for a given objective function. To validate the effectiveness of CQA, extensive simulation experiments were conducted. The experimental results show that compared to traditional simulated annealing (SA) and genetic algorithms (GA), the IMFMN optimized using CQA achieves a minimum increase of 8.63% in regional coverage. This significantly improves the efficiency of industrial machinery fault monitoring and enhances the coverage capacity and performance of the network.