Overlapping Signal Recognition Method for Sealed Relays Based on Machine Learning and Confidence Probability

Component signal seriously affects the loose particle detection results. The existing research focused on pure loose particle and component signals, training suitable classifiers to classify the data of two labels from two signals. However, in real application scenarios, pure signals rarely appear, and the data classification results are not the required signal recognition or loose particle detection results. The feasibility and practicality of the existing research are limited. In this article, the authors proposed a loose particle detection method based on the recognition of overlapping signals. By obtaining the optimal recognition model and standard confidence probability, the pure and overlapping signals can be accurately recognized, and the loose particle detection can be realized in a comprehensive manner. Multiple detection results in real application scenarios indicated that the obtained overlapping signal recognition and loose particle detection results were stable and reliable. Compared with the existing research, the loose particle detection sensitivity has been significantly improved.