Unsupervised Extraction of Shape-Based Signal Patterns for Incoming Signal Recognition

Coming up with a method to extract meaningful information from sensor signals without prior knowledge is one of the important challenges in machine learning. In the context of this challenge, this article develops an unsupervised method to extract high-intensity shape-based signal patterns collected by a spintronic sensor for industrial machine condition monitoring. For this purpose, this article first proposes a feature extraction method inspired by both shapelet transform and wavelet transform. The feature extraction method first extracts unsupervised shapelets, which are then treated as mother wavelets or filters to decompose the signals into their components. In addition, an idea based on the signal symbolic representation is proposed to extract signal patterns of various lengths. The signals are summarized with symbols obtained by finding a high-quality clustering result using an internal clustering validation criterion, which makes it suitable for further data mining tasks using far fewer memory resources. Finally, the signal patterns or symbols extracted from recorded data are then used for real-time signal recognition by a measure proposed to compute the similarity of the incoming signal with all the existing symbols. The performance and efficiency of our approach are demonstrated using real datasets.