Abnormal Sound Detection in Pipes Using a Wireless Microphone and Machine Learning
Kota Notani, Takahiro Hayashi, Naoki Mori
Abstract
Abnormal sound detection using a one-class support vector machine (OCSVM) and a principal component analysis (PCA) is proposed aiming to stable and objective inspection without skilled plant inspectors. For measurement of acoustic signals, we developed a compact microphone unit that can work in sound detection, signal transmission, and power supply, wirelessly. Six signal parameters were extracted as features from filtered and segmented acoustic signals. Using the features standardized and reduced in dimensionality by PCA, an anomaly detection model using OCSVM is built to detect abnormal sounds. The proposed method is verified by acoustic diagnosis of sound waves leaking from pipeworks with running water. Diagnostic accuracies were evaluated for artificial abnormal sounds with different types of burst waves output from a piezoelectric element attached to the pipe and Pencil Lead Break sound in water flowing background noise. Burst wave changes could be detected in almost all patterns, and the diagnostic accuracy was 100% for the Pencil Lead Break sound.