Abstract
The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self-organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP-hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.
Highlights
With the rapid development of the manufacturing industry, machine fault diagnosis plays an increasingly important role in ensuring the safe operation of equipment [1,2,3]
To obtain the maximum monitoring range and prolong the lifetime of Industrial wireless sensor network (IWSN) with a limited number of nodes, this paper proposes a self-organizing IWSN target coverage method based on a hybrid niche immune genetic algorithm (HNIGA) and establishes a corresponding system model
A novel optimization algorithm HNIGA is used to solve the problem of fault detection coverage in IWSN, which follows the framework of conventional heuristic methods and is a randomized search algorithm that draws on natural selection and natural genetic mechanisms of the biological world
Summary
With the rapid development of the manufacturing industry, machine fault diagnosis plays an increasingly important role in ensuring the safe operation of equipment [1,2,3]. Industrial wireless sensor network (IWSN) provides an effective scheme for machine fault diagnosis [4]. Point coverage refers to the random deployment of sensor nodes near some discrete target points in a given area for data collection and monitoring. It is difficult to deploy all industrial wireless sensor nodes to the correct location at the same time by large-scale random deployment method, which is easy to generate unreasonable coverage structure, and form perceptual overlap and blind spot. In this case, the redundant deployment of sensor nodes compensates for the low sensor coverage.
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