19AUGUST 2025Data analytics in condition monitoring is a broad and critical domain, aimed at capturing and interpreting process-related data to ensure machine reliability and efficiencyAdvanced sensors are significantly reducing unplanned downtime in the automotive sector. How are these sensors integrated into existing maintenance workflows to maximize their effectiveness?There are two key approaches to integrating condition monitoring solutions for industrial machinery, depending on whether the equipment is new or already in operation. For newly manufactured machines, we work directly with Original Equipment Manufacturers (OEMs) to integrate sensors into their products, such as gearboxes. These OEMs supply industrial machinery to end users. By embedding vibration sensors in the gearbox during manufacturing, the end user can start capturing and analyzing condition data from day one. This proactive approach ensures that predictive maintenance capabilities are built into the equipment from the outset. For older machines already in use, we collaborate with end users to assess machine criticality and recommend the most suitable condition-monitoring solutions. The selection process considers factors such as operational importance, failure risks, and maintenance needs. In addition to supplying condition monitoring products, we provide comprehensive services to support implementation. This includes annual condition monitoring contracts, where trained specialists are deployed to the customer's plant to conduct continuous monitoring and diagnostics. Remote monitoring and data analysis are also offered, where vibration data is collected from sensors, transmitted to centralized monitoring centers (such as in Pune, India), and used to generate real-time reports for customers.Data analytics plays a crucial role in improving the effectiveness of condition monitoring systems. What specific metrics are most valuable for manufacturers to track?Data analytics in condition monitoring is a broad and critical domain, aimed at capturing and interpreting process-related data to ensure machine reliability and efficiency. The goal is to gather meaningful insights that can help predict potential failures and optimize maintenance schedules. For instance, in the case of a pump, its primary function is to build pressure and deliver flow. If the pump is driven by an electric motor that starts malfunctioning, it will fail to meet the required flow or pressure. Data analytics helps monitor such process parameters, identifying deviations that may indicate underlying issues. If an electric motor begins to deteriorate, the failure could be due to problems with the bearing, rotor, or stator. By fitting vibration sensors on the motor, these issues can be detected early. The collected signals allow analysts to assess the motor's performance at different RPM levels, track trends, and predict potential failures before they occur. A key metric in this analysis is the Mean Time Between Failure (MTBF). Every asset owner aims to maximize this interval, as a longer MTBF signifies stable and controlled operations. When deviations occur from expected metrics, it can indicate problems related to maintenance, lubrication, assembly, or other process-related factors. Different industries have specific performance metrics based on their operational requirements. A paper plant, cement plant, or steel plant will each have unique parameters, but the fundamental priorities remain the same--ensuring reliability and 24/7 equipment availability. Data analytics enables real-time monitoring and predictive maintenance, allowing businesses to maintain smooth operations and minimize unplanned downtime.The integration of predictive maintenance strategies is becoming essential for enhancing machine longevity. What challenges do companies face when transitioning from reactive to predictive maintenance?Predictive maintenance is increasingly being adopted by companies as a key strategy to enhance operational efficiency and prevent unplanned downtime. However, several challenges arise in its implementation, particularly when integrating older machines with Industry 4.0 technologies. Many companies aspire to upgrade their infrastructure to enable seamless machine-to-machine communication, but this often encounters compatibility issues. Older machines may operate on different platforms that do not inherently communicate with one another, creating a significant bottleneck in achieving a unified Industry 4.0 ecosystem.
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