A Generic Framework for Degradation Modeling Based on Fusion of Spectrum Amplitudes

Prognostics and health management aims to use on-line sensor data to monitor and predict current and future health conditions of degraded systems and components. Nowadays, constructing composite health indices for characterizing current health conditions of a system from multiple degradation-based sensor data has attracted much attention. These kinds of “data-level” models show strong ability to provide a better degradation characterization of a degraded system than a model solely depending on data from an individual sensor. Although numerous efforts have been made to propose “data-level” fusion methodologies for process data, such as temperature, pressure, speeds, etc., little research has targeted “data-level” fusion models for nonprocess data, such as vibration and acoustic signals. In this article, a methodology for constructing a composite health index from fusion of spectrum amplitudes is proposed. Here, each spectrum amplitude can be regarded as “an individual sensor.” The goal of this article is that a composite health index generated from fusion of spectrum amplitudes can simultaneously detect incipient faults and provide a monotonically increasing trend for degradation assessment. Our proposed methodology was verified by two illustrative examples including gearbox run-to-failure vibration data and bearing run-to-failure vibration data. Results showed that our proposed methodology is better than popular sparse measures for gear and bearing health monitoring and degradation assessment. Note to Practitioners —Process data, such as temperature, pressure, speed, etc., are capable of directly showing degradation trends of degraded systems and components. “Data-level” fusion models can be directly used to fuse process data from multiple sensors to show a better-fused degradation trend than a sole trend obtained from an individual process data sensor. Being different from process data, nonprocess data, such as vibration and acoustic data, cannot be used to directly show degradation trends unless they are transformed into a health index. One of the benefits of nonprocess data is that they have been proved to be sensitive to incipient machine faults. Nevertheless, due to complicated transmission paths and multiple responses, transforming nonprocess data into a health index is still a challenging task. This article presents a methodology to fuse spectrum amplitudes to form a health index that can simultaneously detect incipient machine faults and assess monotonic machine degradation. The main idea of this article is to regard each spectrum amplitude as “an individual sensor” and the sum of weighted spectrum amplitudes as a health index. To implement the proposed methodology, it is necessary: 1) to transform temporal nonprocess data into frequency spectra by using the well-known Fourier transform; 2) to know two essential properties about detecting incipient machine faults and assessing machine degradation; and 3) to train weights based on the essential properties by any convex optimization algorithms. Once optimal weights are obtained, the health index has ability to simultaneously detect incipient machine faults and monotonically assess machine degradation.