A probabilistic neural network with adaptive capability to optimize fault trees for the intelligent fault diagnosis of rapier loom throughout its operating cycle1
Affiliations: [a]
School of Mechanical Engineering, Hebei University of Technology, Tianjin, China
| [b]
Career Leader Intelligent Control Automation Company, Suqian, Jiangsu Province, China
| [c]
Tianjin Key Lab Power Transmiss & Safety Technol, Dept State Key Lab Reliabil & Intellectual Elect, Tianjin, Peoples R China
Correspondence:
[*]
Corresponding author. Feng Wan, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China. E-mail: 2006123@hebut.edu.cn.
Note: [1] This work was supported in part by the Natural Science Foundation of Hebei Province under Grant E2022202136. No potential conflict of interest was reported by the authors. The datasets in the article are all obtained experimentally. The datasets generated during the current study are available from the corresponding author on reasonable request.
Abstract: The mechanical structures of the rapier loom are strongly coupled, resulting in faults that are characterized by strong coupling, hierarchy, phase dynamics, and a transient nature. However, current fault diagnosis methods using a single approach are not satisfactory. Additionally, fault diagnosis of the entire operation cycle of the rapier loom equipment is lacking. This paper proposes a fault tree diagnosis method with probabilistic neural network optimization to build a complete fault diagnosis system for rapier looms and improve their intelligent diagnosis capability. The method has strong fault tolerance and self-adaptive capability, allowing for accurate location of the root cause of the fault from multiple fault sources. By accumulating fault samples and continuously improving the diagnosis network, the accuracy of diagnosis can be further enhanced. Initially, the failure mechanism of key subsystems of rapier loom is analyzed. A fault tree model is established for each subsystem based on expert experience and historical data. The model identifies the characteristic sign quantities of typical fault types and serves as the basic input for fault identification. A probabilistic neural network is used to train the fault sample set and complete the diagnosis of the cause of the fault. According to field experiments, the proposed method has demonstrated a significant improvement in the efficiency of locating and identifying fault signs in rapier looms. This improvement allows for accurate and quick identification of faults.
