Showing papers by "Yaguo Lei published in 2014"

A data-driven multiplicative fault diagnosis approach for automation processes

Abstract: This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented.

An enhanced stochastic resonance method for weak feature extraction from vibration signals in bearing fault detection

Abstract: Rolling element bearings are widely used in modern machinery and play an important role in industrial applications. Tough environments under which they work make them subject to failure. The classi...

A particle filtering-based approach for remaining useful life predication of rolling element bearings

Abstract: Rolling element bearings are one of the most widely used components in rotating machinery. However, they are also the components which frequently suffer from damage. Remaining useful life (RUL) prediction of rolling element bearings has received considerable attention, since it can avoid failure risks, and ensure availability, reliability and security. Model-based methods are commonly used in RUL prediction because of their high accuracy in long-time prediction. In model-based methods, a degradation indicator which describes the whole degradation process of bearings, however, is very critical but difficult to be extracted. A model function, used to predict the evolution trend and the RUL of bearings, is difficult to develop as well. In this paper, a particle filtering (PF)-based approach is developed to predict the RUL of rolling element bearings. In this approach, two modules are included, i.e. indicator calculation module and PF-based prediction module. In the first module, a new degradation indicator is calculated based on correlation matrix clustering and weight algorithm. This indicator fuses different characteristics of multiple features, includes more fault information and therefore has a better prediction tendency. In the second module, a PF-based approach is proposed to predict the RUL of bearings. Different from the traditional PF-based approach, a new algorithm of parameter initialization is introduced to calculate the initial parameters of the state space model. Experimental data of rolling element bearings are used to demonstrate the effectiveness of this approach. For comparison, another RUL prediction approach based on adaptive neuro-fuzzy inference system (ANFIS) is also utilized to process the experimental data. The result shows that the proposed approach can effectively calculate the appropriate degradation indicator, initialize the model parameters and perform better in RUL prediction than the ANFIS-based approach for rolling element bearings.

Rotating machinery fault detection based on improved ensemble empirical mode decomposition

Abstract: An improved EEMD approach is introduced in this paper based on automatically obtaining the adding white noise amplitude and the ensemble number according to different analyzing signal characteristics. The adding white noise affects decomposition effect is researched in detail, a criterion of adding white noise in EEMD is established, and the improved EEMD algorithm is described. Simulated signals demonstrate the effectiveness of the improved EEMD in diagnosing the faults of rotating machinery. The improved EEMD is successfully applied to an early rub-impact fault detection of machine unit for catalytic cracking of heavy oil, with the fault reason being analyzed in detail. A gear fault detection of hot strip finishing mills is also analyzed utilizing the improved EEMD method. The results show that the improved EEMD can obtain more precise diagnosis results than the original EMD and FFT spectrum.