AI-Empowered Intelligent Instrumentation: From Automatic Meter Reading to Predictive Maintenance

Abstract

The integration of artificial intelligence (AI) into instrumentation and measurement systems is reshaping industrial monitoring, control, and maintenance practices. This article provides a comprehensive overview of AI-empowered intelligent instrumentation, with a focus on three representative application paradigms: automatic meter reading, fault diagnosis for predictive maintenance, and sensor calibration with drift compensation. We review recent advances in deep learning-based object detection for analog and digital meters, highlighting frameworks such as improved YOLO and Fast R-CNN that achieve accuracy exceeding 98% while reducing measurement time by up to 85%. In the domain of prognostics and health management, we examine how convolutional neural networks with time-frequency transformations enable near-perfect fault classification in rotating machinery. Additionally, we discuss AI-driven calibration methods using neural networks and Gaussian process regression, which not only improve accuracy but also provide rigorous uncertainty quantification compatible with international measurement standards. Despite these successes, challenges remain regarding data scarcity, model interpretability, uncertainty quantification, and real-time edge deployment. We conclude by advocating hybrid approaches that combine data-driven AI with conventional model-driven techniques to achieve both high performance and trustworthiness. This review serves as a practical reference for researchers and engineers seeking to adopt AI solutions in instrumentation applications.