Predictive Maintenance in Cyber-Physical Systems Using Streaming Big Data Analytics

Abstract

With respect to the industrial infrastructures, Cyber-Physical Systems (CPS) are considered as one of the fundamental units, where compute, network, and physical processes are combined to support real-time monitoring and decision-autonomy. With the addition of complexity and interconnection of these systems, it is paramount to maintaining the reliability of availability of these systems and reducing unexpected outages. Classical forms of maintenance reactive or pre-planned are inadequate in dynamic situations where degradation patterns of the components evolve with time. To counter this difficulty, this paper suggests a new system architecture that allows streaming big data analytics allowing real-time predictive maintenance in CPS. The framework combines distributed stream processing platforms like Apache Kafka, and Apache Flink and adaptive machine learning models that run at the edge and cloud levels. The system is able to identify anomalies at an early stage, make remaining useful life (RUL) estimates and initiate proactive maintenance practices before failure occurs, by processing exhaustive streams of high-velocity sensor data never ceasing in their processing. One of the most important parts of the proposed architecture is the conduct of concept drift detection and online learning methods, with which the model could adjust to the evolution of system behavior without being retrained. Edge devices are used to preprocess and perform an inference with low latency, whereas updates in the model and long-term analytics are performed in the cloud. The framework was tested on the NASA C-MAPSS turbofan engine degradation dataset and actual-time data of smart manufacturing testbed. Its performance shows that the concept at hand is not only highly accurate (fault classification accuracy exceeding 94 percent with a latency of less than 100 milliseconds), but it also virtually eliminates mean time to failure (MTTF) and improves overall maintenance efficiency. Moreover, the system is robust with concept drift and sensor noise, which is an indication that it is highly applicable to be utilized in the Industry 4.0 environment. The value of this work to the field is that the proposed solution of predictive maintenance of CPS is presented as scalable, adjustable, and the solution that meets the requirements of low latency. It will lead to the era of smarter industry operations based on real-time intelligence.