Predictive capabilities powered by applications at the edge allow businesses to bring intelligence closer to where data is generated for real-time processing.
What Is Predictive Maintenance?
Predictive maintenance is the strategy of diagnosing potential equipment malfunctions in real time in order to prevent failures. The failure of machines or equipment is expensive in terms of repair costs, lost productivity, and missed customer delivery times and expectations.
Technicians have typically conducted routine diagnosis, inspections, and preventive maintenance according to fixed schedules, which is a costly and labor-intensive process. The transition from reactive maintenance to predictive maintenance allows the opportunity to intervene before downtime occurs.
Predictive maintenance can be highly cost effective over traditional preventive maintenance or reactive maintenance strategies. According to Plant Engineering's 2018 maintenance survey, predictive maintenance is favored by 80 percent of maintenance personnel.1
This strategy is designed to operate in the "sweet spot" of optimizing time and money spent on preventive maintenance activity compared to time-consuming reactive maintenance. It provides a foundation for continuous improvement, which enables businesses to reduce maintenance costs, generate cost savings, and improve performance.
How Does Predictive Maintenance Work?
Predictive maintenance uses smart sensors such as machine vision to gather data from equipment, vehicles, or other assets, automating the task of monitoring equipment. This data is analyzed on the spot, triggering an alert if an imminent issue is detected. Machine learning can be used in the cloud or at the edge to combine and analyze data from numerous machines, ensuring the need for maintenance work is accurately predicted.
Today, businesses are moving intelligence closer to data for real-time edge processing. A manufacturing plant might gather data—such as the surface temperature of a motor, the pressure of a hydraulic system, or the liquid level in a tank—wirelessly from a shop floor and use predictive analytics to decide whether values are in the safe and acceptable range. This is a more responsive approach when compared with traditional monitoring of equipment, which involves employees manually checking and maintaining equipment on a predetermined schedule. This type of condition monitoring doesn't provide real-time insights into the status of a specific piece of equipment.
Analysis helps predict the likelihood of future outages and maintenance requirements by using machine learning to understand past failures and then applying those algorithms to current data about a plant and equipment. Models can be continuously trained to become more accurate about predicting maintenance needs.
Machine learning can reveal correlations that weren't possible using condition based maintenance, transforming data into insights about equipment and maintenance requirements.
Machine learning helps predict the likelihood of future outages and maintenance requirements by understanding past failures.
Predictive Maintenance Tools
Hardware and software solutions are key enablers of predictive maintenance programs. For example, energy and utility companies can better understand and manage consumption patterns, transportation companies can optimize service and delivery routes, and manufacturing firms can improve quality assurance and mitigate downtime risk.
High-Performance Intel® Xeon® Scalable Processors
Edge servers based on Intel® Xeon® Scalable processors deliver the high performance to power advanced analytics, with hardware-based security to help keep data secure. Intel® Optane™ DC persistent memory is available on 2nd Generation Intel® Xeon® Scalable processors to help deliver fast insights from data-intensive applications.
Predictive Maintenance Software
Software solutions from Intel partners are custom developed for key focus areas such as fleet predictive maintenance, vehicle predictive maintenance, and computerized maintenance management systems (CMMS). These solutions help reduce the risk of part failure and lower overall costs by optimizing field technician scheduling. Advanced notice of potential failures provides more-efficient part ordering and repairs to mitigate downtime and improve asset management.
Edge Insights for Industrial
Intel's Edge Insights for Industrial provides a foundation to accelerate the deployment of Industrial IoT solutions. It gives businesses the components needed to ingest, process, store, manage, and secure edge-based data across operating systems and industrial protocols. This provides the flexibility to choose the features and capabilities a business wants to include for customized solutions.
Intel® IoT Market Ready Solution (Intel® IMRS)
Intel® IoT Market Ready Solution (Intel® IMRS) for industrial applications help unlock operational efficiency, optimize production, and increase worker safety from the supply chain to the factory floor.
A New Era of Predictive Maintenance
Intel's continued innovation in predictive maintenance is leading to exciting opportunities that help companies avoid lost time and predict maintenance needs with unmatched accuracy. As more data is gathered, predictive maintenance models will continue to improve, delivering even greater value for industrial businesses.