Machine learning technique for data-driven fault detection of nonlinear processes

Saïd, M.; Abdellafou, Khaoula Ben; Taouali, Okba

doi:10.1007/s10845-019-01483-y

Cited by 38 publications

(15 citation statements)

References 51 publications

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“…Their results showed outstanding performance for different operating conditions. Said et al [32] proposed a new method for machine learning flaw detection using the reduced kernel partial least squares method to handle non-linear dynamic systems. Their research resulted in a calculation time reduction and a decrease in the rate of false alarms.…”

Section: Similar Workmentioning

confidence: 99%

Quality 4.0: a review of big data challenges in manufacturing

2021

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Industrial big data and artificial intelligence are propelling a new era of manufacturing, smart manufacturing. Although these driving technologies have the capacity to advance the state of the art in manufacturing, it is not trivial to do so. Current benchmarks of quality, conformance, productivity, and innovation in industrial manufacturing have set a very high bar for machine learning algorithms. A new concept has recently appeared to address this challenge: Quality 4.0. This name was derived from the pursuit of performance excellence during these times of potentially disruptive digital transformation. The hype surrounding artificial intelligence has influenced many quality leaders take an interest in deploying a Quality 4.0 initiative. According to recent surveys, however, 80–87% of the big data projects never generate a sustainable solution. Moreover, surveys have indicated that most quality leaders do not have a clear vision about how to create value of out these technologies. In this manuscript, the process monitoring for quality initiative, Quality 4.0, is reviewed. Then four relevant issues are identified (paradigm, project selection, process redesign and relearning problems) that must be understood and addressed for successful implementation. Based on this study, a novel 7-step problem solving strategy is introduced. The proposed strategy increases the likelihood of successfully deploying this Quality 4.0 initiative.

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Section: Similar Workmentioning

confidence: 99%

Quality 4.0: a review of big data challenges in manufacturing

2021

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“…For complex chemical process, the moving window technique presents good effectiveness compared to other methods [12,30]. But, for example, the moving window RKPLS (MW-RKPLS) is based on the dataset size of the moving window.…”

Section: Related Work Of Dynamic Methodsmentioning

confidence: 99%

“…Afterwards, the FD performances of the suggested method are illustrated in terms of good detection rate (GDR), false alarm rate (FAR), and computation time (CT). In this article, a comparative study of data-driven fault detection and monitoring methods was performed between the proposed method DRKPLS, moving window RKPLS (MW-RKPLS) [12], moving window reduced rank KPCA (MW-RRKPCA) [13], and online reduced rank KPLCA (ORRKPCA) [14]. To conclude, the main contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 99%

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

Saïd

Taouali

2021

Mathematical Problems in Engineering

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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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“…A manufacturing system includes several sub-functions [1,2], such as planning [3], scheduling [4][5][6][7], dispatching [8], machine maintenance [9,10], and quality control and inspection [11,12]. In particular, to facilitate the effective operation of a manufacturing process, automated predictive maintenance operations are preferentially performed via fault detection, diagnosis, and predictions based on sensor signals collected during process execution [9,10,[13][14][15]. For example, Lu et al [16] detected occurrences of bearing faults during operation under harsh conditions (i.e., low signal-to-noise ratio) by applying adaptive stochastic resonance.…”

Section: Introductionmentioning

confidence: 99%

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Baek

2021

Int J Adv Manuf Technol

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As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

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Machine learning technique for data-driven fault detection of nonlinear processes

Cited by 38 publications

References 51 publications

Quality 4.0: a review of big data challenges in manufacturing

Quality 4.0: a review of big data challenges in manufacturing

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

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

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