Signal detection and material identification method for loose particles inside aerospace relays based on overlapping signals

Abstract

Loose particle is an important factor affecting the reliability of aerospace relays. Particle Impact Noise Detection method is a commonly used loose particle detection method, but it can generate interference signals. Accurately identifying loose particle signals and component signals becomes the key to accurately detecting loose particles. Meanwhile, deeply exploring the properties of loose particle signals and further provide material information can provide guidance for manufacturing processes. However, the existing loose particle signal and component signal identification research, as well as the loose particle material identification research, has problems such as limited research objects to pure signals, independent identification results, and failure to refer to the detection requirements in real application scenarios. It is difficult to apply and its practicality is not high. Based on this, the authors proposed a signal detection and material identification method for loose particles based on overlapping signals. Specifically, referring to the latest loose particle detection information, aerospace relay samples were made, and the component identification model and material identification model based on parameter-optimized random forest were trained, respectively. They can achieve good classification effects on data sets created from pure loose particle signals, pure component signals, and pure loose particle signals corresponding to different materials, respectively. On this basis, confidence coefficient was proposed to quantify the degradation degree of the classification effects of the two models on the data set created from overlapping signals, thus the component confidence coefficient and the loose particle confidence coefficient were obtained, respectively. They can be used to determine valid pulses from pure loose particle signals, pure component signals, and mixed signals in overlapping signals, completing loose particle detection. Valid pulses from pure loose particle signals were screened for material identification. In this way, for the aerospace relay to be tested, first, the loose particle detection results can be obtained by a comprehensive judgment of the component identification model, component confidence coefficient, and loose particle confidence coefficient. Second, the material identification results can be obtained by combining the material identification model with the majority voting processing. In addition, the definition of identification accuracy applicable to the loose particle detection was proposed to meet the engineering application requirements in real application scenarios. Multiple experiments verified the feasibility, practicality, and stability of the proposed method.