Predictive Maintenance: Optimize Efficiency of Your Industrial Asset

Predictive maintenance of industrial asset aims to improve on existing maintenance operations to unlock the business value.  

Predictive maintenance capability allows for the optimization of maintenance activities based on actual equipment performance, rather than relying on traditional time-based or run-to-failure maintenance approaches. 

Predictive maintenance system enables end users and OEMs to adapt a proactive and reliability-centered approach to maintenance. It uses predictive models to predict when an asset is likely to fail, enabling you to plan maintenance activities in advance and minimize costly downtime. 

Predictive maintenance can increase the overall equipment effectiveness (OEE) of an industrial asset by up to 5-10% (Source: ReliaSoft Corporation). 

Such systems use data from IoT sensors and other data sources, to maximize uptime by predicting the probable faults.  

This helps reduce the chances of unexpected failure, which can have serious implications for the organization. 

Predictive Maintenance vs. Traditional Maintenance Methods 

Predictive maintenance and traditional maintenance methods have several key differences: 

What are the Benefits of Predictive Analytics in Industrial Asset Maintenance? 

By using real-time data and AI, the system predicts equipment probable faults and failures. It suggests changes required for improving maintenance operations, reducing downtime, increasing equipment efficiency, and lowering maintenance costs.  

Predictive analytics can provide several key benefits in industrial asset maintenance, including: 

• Reduced Downtime: Predictive analytics can help to reduce downtime by predicting and preventing equipment failures, ensuring that maintenance activities are performed before issues arise. 
• Increased Equipment Efficiency: Predictive analytics can help to increase the efficiency of industrial assets by optimizing maintenance activities and reducing unplanned downtime. 
• Lower Maintenance Costs: Predictive analytics can help to reduce maintenance costs by reducing the frequency of maintenance activities and ensuring that maintenance is performed only when necessary. 
• Improved Safety: Predictive analytics can help to improve safety by predicting and preventing equipment failures that could result in accidents or other safety hazards. 
• Better Resource Planning: Predictive analytics can help to better plan maintenance resources by providing insight into the maintenance needs of individual assets, allowing maintenance teams to prioritize their efforts and allocate resources more effectively. 
• Improved Equipment Reliability: Predictive analytics can help to improve the reliability of industrial assets by predicting and preventing equipment failures, reducing unplanned downtime, and increasing equipment efficiency. 
• Data-Driven Decision Making: Predictive analytics provides data-driven insights into the performance of industrial assets, allowing maintenance teams to make informed decisions about maintenance activities and improve the overall maintenance process. 

What are the Different Types of Predictive Maintenance? 

Predictive maintenance techniques involve collecting operating conditions data from an asset, analyzing it in real time, and using the insights to determine when maintenance is performed.  

Predictive data analytics rely on accurate readings of different parameters to take correct measurements of your industrial equipment’s. There are several types of predictive maintenance, including: 

• Condition-Based Monitoring: Involves monitoring the performance of a machine and its components, analyzing data to detect any changes in performance that could indicate a problem, and taking proactive action to address the issue. 

• Predictive Analytics: Uses statistical models and algorithms to analyze data from various sources, such as machine logs and sensor data, to predict future performance and identify potential issues. 

• Machine Learning: Algorithms trained on historical data to identify patterns and trends that can be used to predict future failures. 

• Prognostic Maintenance: Predicts the remaining useful life of a component or machine and schedules maintenance activities based on that prediction. 

• Vibration Analysis: Measures and analyzes the vibration patterns of a machine to detect potential issues and predict when maintenance is required. 

• Oil Analysis: Analyzes the condition of lubricants to detect any contaminants or signs of wear and predict when maintenance is required. 

• Infrared Thermography: Uses infrared cameras to detect heat patterns and identify potential issues in equipment or components. 

• Ultrasonic Testing: Uses high-frequency sound waves to detect potential issues in components and machines. 

Each type of predictive maintenance has its own strengths and weaknesses, and the choice of which type to use depends on the specific requirements of the industrial assets being managed. Some organizations may use a combination of different types of predictive maintenance to achieve the best results. 

How Organizations can Move Towards Predictive Analytics for their Industrial Assets?  

Predictive maintenance can be applied to a variety of industrial assets, including machinery, equipment, and facilities. However, it is important to note that the implementation of predictive maintenance requires a significant investment in technology and data analysis capabilities, as well as the integration of data from multiple sources.  

Implementation of such tools and technologies is a challenging task, but the benefits of predictive maintenance, including reduced downtime and improved efficiency, make it a worthwhile investment for many organizations. 

How you can swiftly move towards predictive analytics for their industrial assets by following these steps: 

1. Assess Current Maintenance Processes 

This process involves assessment of current maintenance processes to identify areas where predictive analytics can provide the most value. This includes identifying the critical assets that are most important to the organization, and the data sources that are available for analysis.  

2. Implement Sensors and Data Collection 

Organizations should implement sensors and other data collection tools to gather data on the performance of industrial assets. This includes data on equipment usage, temperature, vibration, and other key performance indicators. 

3. Clean and Store Data 

Businesses should clean and store the data collected from sensors and other sources in a centralized repository for analysis. This includes removing any irrelevant data, and ensuring that the data is stored in a format that can be easily analyzed. 

4. Analyze Data 

Predictive analytics tools can help you analyze the data from industrial assets. This includes using machine learning algorithms to identify patterns and relationships in the data, and using this information to predict maintenance requirements. 

5. Implement Predictive Maintenance 

Organizations should implement predictive maintenance processes based on the insights gained from the data analysis. This includes scheduling maintenance activities based on the predicted maintenance requirements of individual assets, and using real-time data to optimize the maintenance process. 

6. Monitor and Evaluate 

Businesses should monitor and evaluate the results of the predictive maintenance process, and make changes as necessary to improve the process over time. This includes collecting feedback from maintenance teams and using this information to refine the predictive maintenance process. 

Businesses can move towards predictive analytics for their industrial assets by implementing sensors and data collection, cleaning and storing data, analyzing data, implementing predictive maintenance, and monitoring and evaluating the results. 

Kemsys’s Predictive Asset Maintenance Solution 

Kemsys’s connected asset management solution gives powerful tools for predictive maintenance and condition monitoring for your critical assets. It helps to identify potential problems before they become serious, thereby avoiding costly repairs and downtime.  

Our solution uses a cohesive framework to capture data from connected devices, analyze it in real-time, and predict when an asset might need maintenance or repair. Our solutions can help you to improve operational efficiency, reduced downtime, and better use of resources.  

Conclusion 

Predictive maintenance is a key approach to industrial asset management. It leverages advanced analytics, machine learning, and data from various sources to predict when a machine is likely to fail. This allows maintenance teams to proactively plan maintenance activities, reducing the risk of unplanned downtime and improving the overall performance and efficiency of industrial assets.  

Predictive analytics and automation help businesses to optimize the cost of scheduled maintenance by ensuring that only necessary maintenance tasks are performed on time. Overall, predictive maintenance is an effective way to increase efficiency and reduce costs associated with asset failure.