Predictive maintenance: Maximizing equipment efficiency through condition monitoring

Man in a safety vest looking at his open laptop while standing above a factory floor

Increase uptime and efficiency in your systems

In today's fast-paced industrial landscape, the importance of proactive maintenance strategies cannot be understated. Predictive maintenance, enabled by intelligent drive edge computing and condition monitoring, has emerged as a powerful tool to optimize equipment performance, increase uptime, and reduce maintenance costs. In this article, we will explore the concept of predictive maintenance in relation to condition monitoring and highlight its numerous advantages in terms of economic efficiency, equipment performance, and cost savings.

Advantages of predictive maintenance condition monitoring

Increased economic efficiency

By continually collecting and monitoring data, predictive maintenance ensures that equipment issues are identified before they escalate, minimizing the risk of downtime. This proactive approach enhances economic efficiency by preventing costly unplanned shutdowns and reducing overall maintenance expenses.

Higher uptime

The constant monitoring of data allows for early identification of any changes or abnormalities in equipment performance. By addressing potential issues before they lead to failure, predictive maintenance maximizes equipment uptime, ensuring smooth operations and minimizing disruptions.

Extended machine/equipment lifetime

Through timely maintenance and addressing potential issues proactively, predictive maintenance helps to optimize the performance and lifespan of machines and equipment. By identifying and rectifying issues before they cause severe damage, the need for expensive repairs or premature replacement is significantly reduced.

Optimal preventive maintenance timing

Predictive maintenance utilizes condition monitoring data to determine the best point in time for maintenance activities. By analyzing the health state of components and following the degradation curve, maintenance can be triggered before functional failure occurs. This approach ensures that preventive maintenance activities are carried out when they are most needed, preventing unnecessary downtime and reducing costs associated with reactive repairs.

Edge analytics and machine learning for better machine performance

The collected data from condition monitoring provides valuable insights into the performance of machines and equipment. By analyzing this data using edge analytics and applying machine learning algorithms, manufacturers can identify patterns, trends, and potential areas for improvement. This information can be used to optimize machine efficiency, resulting in better overall performance and increased productivity.

Illustration of a P-f curve depicting a typical degradation pattern
By analyzing this data using edge analytics and applying machine learning algorithms, manufacturers can get early warning of potential failures - and identify patterns, trends, and potential areas for improvement.

Significant savings potential of condition-based maintenance (CBM)

A study commissioned by the European Commission highlights the significant savings that can be achieved through a properly functioning condition-based maintenance (CBM) program. The study estimates savings of 8-12% over traditional preventive maintenance schemes. Additional benefits reported include a reduction in maintenance costs by 14-30%, downtime by 20-45%, breakdowns by 70-75%, and an improvement in production by 15-25%.The algorithm compares the actual cavitation signature against user-defined threshold levels. If the values exceed the set thresholds for a predefined time, the event is flagged as cavitation.

Furthermore, repair costs for failed assets are typically 50% higher than if the problem had been addressed prior to failure. Reports from companies like Fusheng in the compressor industry indicate that timely repairs resulted in a 15% reduction in mean time to repair (MTTR) and a 20% increase in the first-time fix rate.

Bottling line full of green bottles
CBM gave HEINEKEN the power to gather more critical application data in real time than ever before. Furthermore, the drives supported pre-existing communication interfaces and software, meaning HEINEKEN did not have to invest in a new parallel system as part of the upgrade.

How analytics are leveraged in predictive maintenance

Predictive maintenance relies on advanced analytics to leverage the collected data effectively. This includes:

  • analyzing component lifetime and fault information
  • implementing condition-based maintenance strategies
  • establishing baseline information for comparison
  • utilizing machine learning algorithms to identify patterns and make accurate predictions

These analytics enable manufacturers to prevent unexpected issues, optimize availability, reduce wear and tear effects, extend equipment lifetimes, and create predictable long-term cost savings through tailored maintenance plans.

Read how HEINEKEN optimized its Den Bosch production line

Maximize productivity with intelligent edge computing

Predictive maintenance, enabled by intelligent drive edge computing and condition-based monitoring, helps manufacturers achieve optimal equipment performance, increased uptime, and cost savings. By systematically ensuring the optimal condition of machinery and addressing potential issues before they escalate, businesses can avoid unexpected downtime, extend equipment lifetimes, and maximize overall productivity. Embracing predictive maintenance not only reduces complexity but also provides actionable insights that take the guesswork out of maintaining equipment, ensuring a competitive edge in today's rapidly evolving industrial landscape.

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