## 5 Conclusions  
The comparative study presented in this paper demonstrates the effectiveness of preprocessing methods for detecting stiction in industrial control valves using ANN based on pattern recognition. By leveraging PVs and OPs, the D-value method accurately identifies stiction within control loops. The ANN, trained with preprocessed data from a robust data-driven model through the application of two methods, namely, the D-value and PCA, was validated and tested with real industrial data from the ISDB.  
This study's evaluation of two preprocessing methods revealed a significant impact on fault detection accuracy, with an overall accuracy of 76%, an 86% precision rate in stiction prediction, and a 66% success rate in nonstiction scenarios when using D-value preprocessing. This indicates that the pattern between PV and OP is crucial in detecting the stiction fault. Implementing the data-driven model in SIMULINK and training the ANN in MATLAB with the Pattern Recognition Toolbox underscore the practical application and reliability of the method.  
These promising results indicate that the fault detection technique with D-value preprocessing can reliably diagnose stiction in industrial settings, offering a valuable tool for enhancing maintenance protocols, reducing operational downtime, and improving overall efficiency. Future research will focus on broadening the method's applicability to a diverse range of control systems and operational conditions, further solidifying its value in industrial practices. Integrating this technique into existing control systems is anticipated to drive significant improvements in system performance and reliability. It is evident from the performed experiment that using the D-value for data processing yields better prediction accuracy than PCA. This is because the D-value, which is based on the centroid principle, is better at representing stiction properties than PCA, which is based on the covariance of variables.  
Sensor fusion is the ultimate form of sensor integration. By reducing the computational complexity and size to increase the accuracy, reliability, and performance, sensor fusion has been adopted in many industries to generate better outputs. The added layer of redundancy always benefits the clarity of the result and its error diagnosis.  
Through sensor fusion applications, as we briefed about in this paper, complex fault identification, classification, and diagnosis tasks are made more straightforward. Correlating them with most modern technology innovations such as neural networks and machine learning has become a breakthrough in the quality control process.  
Conceptualization, S.K.V. and B.R.N.; methodology, B.R.N., S.K.V. and V.S.; software, B.R.N.; validation, B.R.N. and S.K.V.; formal analysis, V.S.; investigation, B.R.N.; writing--original draft preparation, B.R.N., V.S. and S.K.V.; writing--review and editing, S.K.V.; supervision, S.K.V.; project administration, S.K.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.