Incipient fault detection with smoothing techniques in statistical process monitoring

Ji, Hongquan; He, Xiao; Shang, Jun; Zhou, Donghua

doi:10.1016/j.conengprac.2017.03.001

Cited by 147 publications

(62 citation statements)

References 34 publications

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“…In the PCA model, two PCs are retained, which account for more than 96 % of the variance in original variables. For the DPCA method, the time lag is determined as 1 . The test dataset containing a sensor precision degradation fault consists of 2000 samples as well.…”

Section: Case Studiesmentioning

confidence: 99%

Diagnosis of sensor precision degradation using Kullback‐Leibler divergence

Zhou

2017

Can J Chem Eng

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For practical industrial processes, detection and isolation of a sensor precision degradation fault is of vital importance. In comparison with other sensor fault types such as complete failure and mean shift, the precision degradation fault is usually more difficult to detect and further to isolate. This paper proposes a new fault detection algorithm for sensor precision degradation using Kullback-Leibler divergence (KLD). The KLD is employed to quantify the dissimilarity between probability densities of each reference score and the actual one within the principal component analysis (PCA) framework. Under the assumption of Gaussian-distributed data, KLD for each score reduces to a measure of the variance difference, which is proven effective in detecting sensor precision degradation. Control limit of the KLD is established based on historical data, and online fault detection is implemented by adopting a sliding window. The fault isolation issue is also discussed, which aims to determine the faulty sensor with precision degradation. This task may be achieved by checking the PCA loading matrix and matching the fault detection result. Case studies on a synthetic numerical example and the continuous stirred tank reactor (CSTR) process are carried out to demonstrate the effectiveness of the proposed method, in comparison with conventional approaches.

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Section: Case Studiesmentioning

confidence: 99%

Diagnosis of sensor precision degradation using Kullback‐Leibler divergence

Zhou

2017

Can J Chem Eng

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“…Among those components, the corrosion, shedding and degradation of one component may easily spread the local fault and propagate into a major fault in the system level, which may cause unexpected losses. It is very important to analyze system performance degradation [1,2] and prevent fault propagation. Therefore, the timely diagnosis of a running gears system plays a key role in ensuring the safe operation of trains.…”

Section: Introductionmentioning

confidence: 99%

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System

Cheng

Wang

Luo

et al. 2020

Sensors

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As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.

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“…In process monitoring in chemical engineering industries, predicting the RUL of core equipment has become important in avoiding accidents and economic losses. This is because predictive information, including expectation and probability density function (PDF) of RUL, can help engineers evaluate the operation safety with monitored data and create reasonable maintenance strategies such as the replacement of aging components, in addition to advanced fault diagnosis and fault tolerant control technologies . Therefore, RUL predictions of complicated degradation processes deserve careful attention from the reliability community.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for fractional degradation processes under varying modes

Zhou

Chen

2019

Can J Chem Eng

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Degradation processes of practical chemical engineering systems are difficult to model accurately because of complicated nonlinearities, non-stationarities, and non-Markovian properties. Traditional prognostics techniques tend to neglect the multi-mode switching issue, and therefore cannot be used for predicting the remaining useful life (RUL) of a piece of long-life equipment, especially when it is continuously operated under varying modes. Specifically, the stationarity of differential data also plays an important role in depicting the evolutionary trend, and further affects the extrapolation procedure for RUL prediction. In this paper, we construct a multi-mode degradation model with regard to fractional diffusion processes by considering both stationary and non-stationary increments. The drift term is represented as a time-related nonlinear function, while the diffusion term is driven by fractional Brownian motion (FBM) or sub-fractional Brownian motion (sub-FBM), according to the results of the stationary test. Based on the monitored data, the model is identified by combining change-point detection and parameter estimation. The closed-form distribution of RUL is then derived through a weak convergence transformation. A numerical example and a case study of a large blast furnace are provided to evaluate the performance of the proposed prognostics framework. K E Y W O R D S degradation, multi-mode, non-Markovian, remaining useful life, stationary test

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Incipient fault detection with smoothing techniques in statistical process monitoring

Cited by 147 publications

References 34 publications

Diagnosis of sensor precision degradation using Kullback‐Leibler divergence

Diagnosis of sensor precision degradation using Kullback‐Leibler divergence

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System

Remaining useful life prediction for fractional degradation processes under varying modes

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