Semi-supervised kernel partial least squares fault detection and identification approach with application to HGPWLTP

2016 ◽  
Vol 30 (7) ◽  
pp. 377-385 ◽  
Author(s):  
Qilong Jia ◽  
Wenyou Du ◽  
Yingwei Zhang
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Kernel Partial Least Squares ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Identification Approach

Process Fault Detection Using Directional Kernel Partial Least Squares

2015 ◽  
Vol 54 (9) ◽  
pp. 2509-2518 ◽  
Author(s):  
Yingwei Zhang ◽  
Wenyou Du ◽  
Yunpeng Fan ◽  
Lingjun Zhang
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Kernel Partial Least Squares

scholarly journals Effective fault detection in structural health monitoring systems

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987323 ◽  
Author(s):  
Marwa Chaabane ◽  
Majdi Mansouri ◽  
Kamaleldin Abodayeh ◽  
Ahmed Ben Hamida ◽  
Hazem Nounou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Random Noise ◽  
Ratio Test ◽  
Multiscale Representation ◽  
Kernel Partial Least Squares ◽  
Partial Least Squares Model ◽  
Partial Least Squares Method

A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110559
Author(s):  
Zelin Ren ◽  
Yongqiang Tang ◽  
Wensheng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Nearest Neighbor ◽  
Fault Isolation ◽  
K Nearest Neighbor ◽  
Kernel Partial Least Squares ◽  
Non Linear ◽  
Nearest Neighbor Rule

The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.

Observer Based Fault Detection and Identification in Differential Algebraic Equations

2013 ◽  
Author(s):  
Jason R. Scott ◽  
Stephen L. Campbell
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Mechanical Systems ◽  
Differential Algebraic Equations ◽  
Algebraic Equations ◽  
Frequency Filtering ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Filtering Technique

Fault detection and identification (FDI) are important tasks in most modern industrial and mechanical systems and processes. Many of these systems are most naturally modeled by differential algebraic equations. One approach to FDI is based on the use of observers and filters to detect and identify faults. The method presented here uses the least squares completion to compute an ODE that contains the solution of the DAE and applies the observer directly to this ODE. Robustness with respect to disturbances is also addressed by a frequency filtering technique.

scholarly journals Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Maroua Said ◽  
Okba Taouali
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Computational Cost ◽  
Nonlinear Process ◽  
Kernel Functions ◽  
Data Driven ◽  
Reduced Model ◽  
Tennessee Eastman Process ◽  
Kernel Partial Least Squares

We suggest in this article a dynamic reduced algorithm in order to enhance the monitoring abilities of nonlinear processes. Dynamic fault detection using data-driven methods is among the key technologies, which shows its ability to improve the performance of dynamic systems. Among the data-driven techniques, we find the kernel partial least squares (KPLS) which is presented as an interesting method for fault detection and monitoring in industrial systems. The dynamic reduced KPLS method is proposed for the fault detection procedure in order to use the advantages of the reduced KPLS models in online mode. Furthermore, the suggested method is developed to monitor the time-varying dynamic system and also update the model of reduced reference. The reduced model is used to minimize the computational cost and time and also to choose a reduced set of kernel functions. Indeed, the dynamic reduced KPLS allows adaptation of the reduced model, observation by observation, without the risk of losing or deleting important information. For each observation, the update of the model is available if and only if a further normal observation that contains new pertinent information is present. The general principle is to take only the normal and the important new observation in the feature space. Then the reduced set is built for the fault detection in the online phase based on a quadratic prediction error chart. Thereafter, the Tennessee Eastman process and air quality are used to precise the performances of the suggested methods. The simulation results of the dynamic reduced KPLS method are compared with the standard one.

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