Multi-Source Sensing and Analysis for Machine-Array Condition Monitoring

Early detection of damage in machines can eliminate the expenses and safety hazards associated with failure. Current methods use distributed systems to monitor individual machines, but the associated costs of instrumentation, data acquisition hardware, and facility retrofits are high. A centralized, remote, multi-source monitoring system would increase both the cost efficiency and ease of user operation. This study investigates how data from multiple sensing streams can best be utilized for decision making processes and to what extent a remote, centralized instrumentation package can effectively monitor multiple machines. An array of duct fans was used in this study to represent an arbitrary set of systems from which data can be collected and fused. Data was acquired using multiple measurement types (vibrations, acoustics, current, voltage, RF) using a centralized instrumentation system. The voltage and current were measured from the single supply used to power the fan array. The remaining sensors, including an accelerometer, microphone, and antenna, were placed in a single, central location among the array of fans. Data analysis focused on determining whether separate, nominally identical machines could be uniquely identified and characterized from measurements. Spectral analysis and signature development were used to characterize the state of each machine. These methods can be implemented in other applications involving the fusion of data from several sources to obtain information about the identification, location, and characterization of one or more dynamic systems.