The global market for Predictive Maintenance is projected to reach US$10 billion by 2025, driven by the growing value of predictive intelligence in asset management. Given that effective plant asset management and maintenance is the cornerstone for achieving excellence in manufacturing productivity, there is significant focus shed on technologies that help in more smart and efficient asset monitoring and maintenance. Legacy maintenance programs which are primarily static in nature and characterized by time-based manual inspection are growing out of favor and are being replaced by newer concepts such as predictive maintenance (PdM). Defined as a strategy where maintenance is scheduled at a future date based on analysis of sensor data measurements, PdM is proactive and is designed to increase reliability of machine and decrease their downtime. PdM is an advanced form of maintenance that offers benefits over and above condition-based maintenance (CbM) which utilizes real-time sensor measurements to identify if a machine or its components have reached a point of failure and then schedule maintenance. Benefits of PdM include advanced prediction of maintenance need; reduces unexpected downtime by over 50%; enables automation of maintenance tasks and reduces repair and overhaul time by 60%; helps in cost savings to the tune of 30% to 40%; timely verification/inspection of equipment; reduction in machine failures; reduction in maintenance costs; over 30% reduced need to maintain inventory of spare parts; extended service life of machines and components and higher equipment ROI; increase in production productivity by over 25% and uptime by 30%; improved operator safety; and over 50% increase in revenues achieved via faster time-to-market. Higher revenues and profits are also associated with improved product quality and reduced risk of manufacturing faults which pushes up the cost of reprocessing.

IoT brings whole new value to predictive analytics in ways hitherto unimagined by making data more readily available and accessible. An efficient IIoT predictive maintenance architecture involves identifying key variables which determine the health of equipment i.e. temperature, voltage, discharge, lubricating oil viscosity. Condition monitoring sensors are then added to the equipment to gather continuous data pertinent to the parameters identified and relay the same via internet to the cloud server. Sensor data passing onto the cloud need to filtered and reprocessed by physical devices called "field gateways". Once filtered and reprocessed, a cloud gateway validates the data for security threats and then provides access to the cloud where the data is pushed to a streaming data processor which accelerates the data's journey to the storage component called the "data lake". From the data lake, the sensor data in its raw unstructured form then moves to the "big data warehouse", where the data is cleaned, cleansed and prepared for analysis by machine learning (ML) algorithms. ML algorithms then process the data to uncover hidden correlations in data sets to discover data patterns and anomalies. Predictive models are then built and with AI are trained to predict problems and failure with exploratory analytics that uses various technical assumptions to make a prediction. The United States and Europe represent large markets worldwide with a combined share of 58.4% of the market. China ranks as the fastest growing market with a CAGR of 31.5% over the analysis period supported by the country's powerful economic planning aimed at enhancing its manufacturing competitiveness as it seeks to integrate into the global manufacturing chain dominated by industrialized economies such as EU, Germany and the United States and move from being a low cost competitor to a direct added-value competitor. At the top of the priority list for China is the migration from a low value added export oriented manufacturing platform to a global industrial power.