Predictive maintenance for industry 4.0 is a method of preventing asset failure by analyzing production data to identify patterns and predict issues before they happen.
Until now, factory managers and machine operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime. In addition to consuming unnecessary resources and driving productivity losses, half of all preventive maintenance activities are ineffective.
It is not a surprise therefore, that predictive maintenance has quickly emerged as a leading Industry 4.0 use case for manufacturers and asset managers. Implementing industrial IoT technologies to monitor asset health optimize maintenance schedules, and gaining real-time alerts to operational risks, allow manufacturers to lower service costs, maximize uptime, and improve production throughput.
How does IoT predictive maintenance work?
For predictive maintenance to be carried out on an industrial asset, the following base components are required:
Sensors – data-collecting sensors installed in the physical product or machine
Data communication – the communication system that allows data to securely flow between the monitored asset and the central data store
Central data store – the central data hub in which asset data (from OT systems), and business data (from IT systems) are stored, processed and analyzed; either on-premise or on-cloud
Predictive analytics – predictive analytics algorithms applied to the aggregated data to recognize patterns and generate insights in the form of dashboards and alerts
Root cause analysis – data analysis tools used by maintenance and process engineers to investigate the insights and determine the corrective action to be performed
Production asset data is streamed from the sensors to a central repository using industrial communication protocols and gateways. Business data from ERP and MES systems, together with manufacturing process flows, are integrated into the central data repository to provide context to the production asset data. Then, predictive analytics algorithms are applied to provide insights for reducing downtime, which are investigated using root cause analysis software.
The benefits of predictive maintenance.
Corporate get a range of business benefits from predictive maintenance. The advantages of PdM include:
Reduced maintenance time– Automatic reports for strategic maintenance scheduling and proactive repairs alone reduces maintenance time by 20–50 percent and decreases overall maintenance costs by 5–10 percent. These insights save time and money.
Increased efficiency– analytics-driven insights improve OEE (overall equipment effectiveness) by reducing unnecessary maintenance, extend asset life and enable root cause analysis of a system to uncover issues ahead of failure.
Improved customer satisfaction- Send customers automated alerts when parts need to be replaced and suggest timely maintenance services to boost satisfaction and provide a greater measure of predictability.
Competitive advantage- Predictive maintenance strengthens company branding and value to customers, differentiating their products from the competition and allowing them to provide continuous benefit in-market.
What is the difference between preventive and predictive maintenance?
Manufacturers have been carrying out different forms of preventive and predictive maintenance for years. Understanding the difference between them, however, is critical with the emergence of Industry 4.0.
Preventive maintenance depends on visual inspections, followed by routine asset monitoring that provide limited, objective information about the condition of the machine or system. In this process, manufacturers regularly maintain and repair a machine to prevent failure.
On the other hand, PdM is data-driven and relies on analytics insights for maintenance and repairs ahead of disruptions in production.