4 Benefits Of IoT-Based Predictive Maintenance

Businesses in machine-intensive industries struggle to maintain a balance between keeping costs down and machines availability up. Effective equipment management and maintenance play a key role in ensuring that this balance is maintained – enabling these businesses to comply with regulations, avoid the costs associated with unnecessary maintenance, and avoid unplanned downtime. Predictive maintenance will help optimize this management process and save on maintenace costs as much as possible.

What is predictive maintenance?

Predictive maintenance is performed by evaluating  the performance of equipment through periodic or continuous equipment condition monitoring. Data captured by IoT devices connecting different machines and systems enable businesses to predict, plan, and take proactive steps for any events like parts repair or equipment failure before it occurs. This type of maintenance is mostly performed while the equipment is operating under normal working conditions to avoid any disruption in the business.

What is the role of IoT in predictive maintenance?

IoT-based predictive maintenance involves the collection of machine data (such as operating temperature, supply voltage, current, and vibration) through sensors and wireless transmission of the collected data to a cloud-based centralized data storage platform in real time.

Maintenance teams then gather data from the centralized data storage system and analyze it using predictive analytics programs and machine learning (ML) algorithms to derive actionable insights.

What are the Benefits of Predictive Maintenance

Reduce Maintenance Costs

Each machine has multiple associated costs, and failure cost haywires your maintenance budget. Therefore, companies can save money by being able to predict and avoid equipment failure as much as possible. In manufacturing industries, improving maintenance planning can result in huge savings. IoT-based predictive maintenance utilizes historical data from multiple sources including IoT devices and sensors to make accurate predictions about machine health, utilization, and the possibility of failure, enabling you to take action based on this information. This allows you to systematically schedule the optimal maintenance and inspection routine to avoid unplanned downtime and unnecessary effort. Avoidable costs can be reduced greatly and you can also reduce the amount of time the machinery or equipment is down for maintenance.

Increase equipment Utilization

Unplanned downtime due to equipment failure, costs incurred due to production delays, and expensive maintenance and repairs drive down profitability. IoT-based predictive maintenance enables more efficient use of existing equipment by providing the ability to predict machine failures and reduce maintenance issues. It can help identify the causes of delays, whether they’re internal or external, and help set up processes to address these causes. You can also detect equipment issues before they become operational, and provide early warnings thus improving equipment availability, reliability and performance.

Extend machines Life

IoT-based predictive maintenance enables you to monitor, maintain, and optimize equipment for better availability, utilization and performance. You can gain better visibility into equipment via real-time monitoring, allowing you to predict machine failure and identify parts that need replacement. You can schedule maintenance and repairs, predict events before they occur, and get real-time notifications that enable you to take quick action, thereby extending the life of your equipment.

Improve Safety and Compliance

Predictive equipment maintenance enables companies to anticipate and address possible safety risks and predict potential issues before they impact workers. They can quickly take appropriate action to mitigate safety risks by analyzing data from multiple sources –both internal and external sources along with the data generated from IoT devices and sensors. By analyzing data over long periods of time, you can identify potentially hazardous conditions and estimate its impact on working conditions. By integrating with human capital management (HCM) solutions, you could then trigger instructions to reallocate resources and keep exposure levels below the threshold values, in compliance with regulations.

Entering the era of 4.0 and 5G, it is now easy to see the tangible benefits of connecting tools and machinery to the Internet to assess their functionality. Rather than setting downtime for machinery only to find they are all working perfectly fine, why not instead harness the power of data modeling to make projections for needs-based maintenance.

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