Predictive Maintenance on Industry 4.0
Overview:
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.
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, allows manufacturers to lower service costs, maximize uptime, and improve production throughput.
How does Faststream Technologies IoT-based predictive maintenance work?
- For predictive maintenance to be carried out on an industrial asset, the following base components are used by Faststream Technologies:
- Sensors – data-collecting sensors installed in the physical product or machine
- Data communication – the communication system that allows the data to securely flow between the monitored asset and the central datastore
- 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 are 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
The benefits of predictive maintenance with Faststream Technologies:
- Manufacturers and their customers get a range of business benefits from Faststream’s predictive maintenance. The advantages of our Predictive Maintenance include:
- 1. Reduced maintenance time– Faststream Technologies automatic reports for strategic maintenance scheduling and proactive repairs alone reduce maintenance time by 20–50 percent and decrease overall maintenance costs by 5–10 percent. These insights save the manufacturer and their customers time and money.
- 2. Increased efficiency– Our 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.
- 3. New revenue streams- Manufacturers can monetize our industrial predictive maintenance by offering analytics-driven services for their customers, including Predictive Maintenance dashboards, optimized maintenance schedules, or a technician dispatch service before parts need replacement. The ability to provide digital services to customers based on data presents an opportunity for recurring revenue streams and a new growth engine for companies.
- 4. Improved customer satisfaction– Our PDM Solutions 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.
- 5. Competitive advantage– Faststream Technologies Predictive maintenance strengthens company branding and value to customers, differentiating their products from the competition and allowing them to provide continuous benefit in-market.
Our Predictive maintenance tools:
- Implementing predictive maintenance requires a baseline of integrated tools.
- Predictive maintenance tools include an industrial IoT platform to model, simulate, test and deploy the predictive maintenance solution.
- The tools include industrial data integration and data analytics algorithms to detect patterns in machine data, and root cause analysis tools for investigating the derived insights and determining the corrective action to be taken.
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 provides 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, Predictive Maintenance is data-driven and relies on analytics insights for maintenance and repairs ahead of disruptions in production.
How are Companies using our IoT-based predictive maintenance tools?
- Organizations are implementing predictive maintenance analytics in a range of ways, from targeted solutions for a single machine part, to factory-wide deployments for increasing OEE throughout the production line.
- For machine and parts manufacturers, a relatively common predictive maintenance use case is monitoring and analyzing the condition of a motor to get alerts about its productivity levels, power consumption, health status, and internal wear.
- Another powerful use case of predictive maintenance is minimizing production defects and reducing waste. Often referred to as Quality 4.0, such implementations can predict when the number of defective products is likely to exceed a threshold percentage and provide the root causes for the expected failure.
- Manufacturers are also turning to predictive maintenance for Factory 4.0, or a connected factory, by installing sensors in machines, workstations, and other designated sites such as the HVAC, security cameras, or worker equipment, to predict issues across the factory floor.