No abstract

No abstract

No abstract

Artificial intelligence (AI)-enabled Industrial Internet of Things (IIoT) marks the rise of systems at the convergence of tremendous amounts of data from multiple IoT devices for complex machine learning/AI software that supports decision making and predictive maintenance in various industries. However, the omnipresent neglect of data quality leads to the accumulation of dark data and the impregnation of biases in AI systems. We address the problem of taming data quality in AI-enabled IIoT systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. These pipelines generate services for (i) erroneous data repair and (ii) unsupervised detection of events and deviations in sensor data. We present the design and deployment of our approach from an AI Engineering perspective using two industrial case studies. THE INDUSTRIAL INTERNET OF THINGS(IIOT) revolutionizes several industries, such as manufacturing, transportation, and energy. It is a major driving force behind Industry 4.0 and employs Artificial Intelligence (AI) techniques, e.g., Machine Learning (ML), to exploit the massive interconnection and large volumes of IIoT data. AI-enabled Industrial IoT systems (IIoTs) improve decision-making [1] and perform predictive maintenance [2] (e.g., tool wear and product defect prediction in the manufacturing domain) in industrial processes. The quality and continuity of IIoT data is a bottleneck and makes these systems rather conservative in what they can achieve. Furthermore, the growing neglect of data quality in AI-enabled IIoTs [3] leads to the accumulation of dark data (unstructured, untagged, and untapped data not analyzed) [4] and the impregnation of biases [5].IIoT data endures a long journey on the edgecloud continuum: (i) data obtained by sensors observing industrial processes is consumed by a rugged industrial computer to control actuators, such as a machine tool in manufacturing; (ii) it is transferred to an edge device over wired/wireless communication channels using industrial communication protocols (e.g., OPC-UA, OPC-DA, NMEA, Bluetooth); and (iii) it is aggregated on edge to be transferred to the cloud using API protocols (e.g., REST, RPC, SOAP, GraphQL). Taming data quality in AI-enabled IIoTs aims to detect and manage data quality issues (bias, freezing, precision degradation, data drift in sensors) on this journey and preserve data continuity on the edge-cloud continuum. Sensor bias is an offset shifting sensor output by a constant value. A sensor freezes when its output is constant in successive samples. Precision degradation occurs IT Professional

The Industrial Internet of Things (IIoT) leverages thousands of interconnected sensors and computing devices to monitor and control large and complex industrial processes. Machine learning (ML) applications in IIoT use data acquired from multiple sensors to perform tasks such as predictive maintenance. While remembering useful learning from the past, these applications need to adapt learning for evolving sensor data stemming from changes in industrial processes and environmental conditions. This paper presents a continual learning pipeline to learn from the evolving data while replaying selected parts of the old data. The pipeline is configured to produce ML experiences (e.g., training a baseline neural network model), improve the baseline model with the new data while replaying part of the old data, and infer/predict using a specific model version given a stream of IIoT sensor data. We have evaluated our approach from an AI Engineering perspective using three industrial case studies, i.e., predicting tool wear, remaining useful lifetime, and anomalies from sensor data acquired from CNC machining and broaching operations. Our results show that configuring experiences for replay-driven continual learning allows dynamic maintenance of ML performance on evolving data while minimizing the excessive accumulation of legacy sensor data.