A Multisensor Information Fusion Method for High-Reliability Fault Diagnosis of Rotating Machinery

Recent advancements in smart sensors and deep learning facilitate the use of intelligent systems for machine health monitoring and diagnostics. While data-driven diagnosis methods can extract meaningful fault patterns automatically from sensor measurements, the reliability of such a bottom-up built system largely relies on the assumption—sensor readings are normal, without outliers, and spurious readings. However, complex industrial environments or hardware malfunction is likely to cause noisy or corrupted sensor readings, resulting in degraded diagnosis performance. This article proposes a multisensor-based framework for fault diagnosis of rotating machinery based on deep learning and data fusion techniques, integrating thermal imaging with vibration measurements. In contrast to the single-sensor method, the proposed method offers two advantages: improved robustness to background noise and diagnostic performance in analyzing corrupted sensor readings. Three case studies are carried out to validate the effectiveness of the proposed method for multifault diagnosis of rotating machinery. The performance and trustworthiness of the system are studied and compared via analyzing normal sensor data, data with different noise levels, and data with sensor anomaly (bias fault and stuck fault). The results demonstrate that the proposed data fusion method presents a high diagnostic performance in identifying machine health conditions in a complex working environment.