Wang Gong

Milling tool wear monitoring through time-frequency analysis of sensory signals

The states of milling tool are closely related to the quality of the workpieces under machining. A high quality product often implies high quality surface finish and dimensional accuracy. Therefore, tool wear has to be controlled. However, the tool wear cannot be measured continuously while the machine is still in operation. Thus an alterative condition monitoring approach should be adopted. The condition parameters, e.g. electric current, vibrations, acoustic emissions, are considered as indirect data in data-driven health management technology as they are not directly related with the machine health states. The sensory signals acquired during the operational process are generally time varying (TV) and non-stationary. The features will be lost if the signals are analyzed from just the time domain or frequency domain. The combination of time and frequency analysis (TFA) of the signals is very useful to extract the features hidden in the signals.

A data-driven prognostics approach for RUL based on principle component and instance learning

The research of Remaining Useful Life (RUL) estimation is one of the most common tasks of Prognostics and Health Management (PHM). This paper presents a data-driven approach for estimating RUL using principle component and instance learning. The approach is especially suitable for situations in which abundant run-to-failure (RtF) data are available. Firstly, the principal component analysis (PCA) is used to find the low-dimensional principal components (PCs) from the statistical features of the measured signals. Then, the health indicators (HI) can be obtained by using weighted Euclid distance (WED), and regressed by the data-driven methods or model-based methods. Finally, the method based on instance learning is employed to estimate the RUL of the machine under operation. The performance of the prognostics approach introduced in this paper is demonstrated by using turbofan engine degradation simulation data set, which is supplied by NASA Ames.

An ensemble model for engineered systems prognostics combining health index synthesis approach and particle filtering

Qual Reliab Engng Int. 2017;1–15. Abstract Prognostics, in other words, remaining useful life (RUL) estimation is a core task of prognostics and health management (PHM). Reliable RUL predictions can reduce maintenance costs, improve production efficiency, and avoid unexpected downtime. Lots of models for RUL predictions have been proposed; however, noise and the nonlinear nature of degradation phenomena often leads to poor prognostics results, and the acquired engineered system data are usually subject to a high level of uncertainty. This makes the RUL estimation models less than satisfactory. Accurate RUL estimation and prediction not only rely on an accurate model but also depend on the adjustments of model parameters to track the variation. In this paper, an ensemble model combining the health index synthesis (HIS) approach and improved particle filtering (PF) is introduced. HIS approach was used to obtain the synthesized health index (SHI) for an engineered system with multiple sensors, which indicated the systems degradation model, while the improved PF approach was used to adjust the parameters of the degradation model obtained from the HIS approach and optimized the RUL estimation results. The performance of the prognostics approach introduced in this paper was demonstrated by using turbofan engine degradation data sets, which was supplied by NASA Ames, and results were compared with several usually used methods.