Exploring Explainable Artificial Intelligence for Trustworthy Sensor Quality Assessment in Smart Industries

Abstract

Explainable Machine Learning (XML) has revolutionized the field of machine learning by offering interpretability, detailed explanations, and enhanced decision-making abilities. It allows for a comprehensive understanding of complex machine learning models, opening pathways for the explicit clarification of their functionalities. In relation to smart industries, where the accuracy and dependability of sensor data are crucial, XML provides a promising solution for evaluating sensor quality and establishing their trustworthiness. This paper introduces an innovative framework that employs XML techniques to interpret the quality of sensors in intelligent industrial settings, thus, produce comprehensible explanations. The framework integrates feature extraction, model training, and interpretability methods to achieve its goals. Initially, sensor data is gathered from a variety of sources, and pertinent features are isolated to identify critical attributes. Subsequently, machine learning models are trained using the compiled data to comprehend the patterns and correlations between sensor readings and their dependability. The proposed framework aspires to predict the reliability of sensors, facilitating informed decision-making and improving overall operational productivity.
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