Currently achieving operational excellence relies heavily on manual effort, the need for a flexible engineering and maintenance workforce. To effectively manage a range of assets, significant investments in human resources are required, both from within the organization and through external suppliers.
Furthermore, the complexity of equipment and systems involved in maintenance often surpasses what individual organizations can handle during outages or emergencies.
In response to these challenges many companies have implemented strategies for asset management. One such strategy is Predictive maintenance systems, which aim to enhance problem identification, maintenance schedules and associated costs.
According to research by Research Nester, the predictive maintenance market is projected to reach a revenue of USD 81,582.5 Million by 2031 with a compound annual growth rate (CAGR) of 31.9% during the forecast period from 2022 to 2031. In 2021, this market generated a revenue of USD 5,261.4 Million.
Predictive maintenance isn’t a trendy term; it represents the future of effective asset management. By maximizing equipment uptime reducing maintenance expenses and enhancing safety measures, predictive maintenance has become a game changer across industries. Based on observations it has been found that predictive[R1] maintenance offers cost savings compared to preventive maintenance with approximately 8% to 12% lower costs. Moreover, when compared to reactive maintenance the cost savings can be as high, as 40%.
The Evolution of Maintenance Practices
Traditionally, maintenance practices have typically followed either a reactive or preventive approach. Reactive maintenance involves addressing equipment failures as they happen leading to downtime, expensive repairs and reduced productivity.
On the other hand, preventive maintenance relies on scheduled inspections and regular maintenance tasks regardless of the condition of the equipment. While this approach reduces the chances of breakdowns it can be inefficient and costly.
According to a survey conducted by Plant Engineering in 2018 about maintenance practices, 80[R2] % of maintenance personnel prefer preventive maintenance. The same survey also revealed that the utilization of maintenance increased from 47%, to 51% while relying on equipment until failure decreased from 61% to 57%.
Benefits of Predictive Maintenance
Predictive maintenance brings advantages to asset management by enhancing equipment uptime reducing maintenance expenses and ensuring safety. By identifying issues before they escalate into major problems companies can avoid costly downtime and repairs while safeguarding their employees.
Research indicates that predictive maintenance has the potential to lower maintenance costs by 12[R3] % and increase equipment uptime by up to 9%. This is possible because companies can optimize the scheduling of their maintenance activities based on real time equipment performance data. By doing they can steer clear of unnecessary repairs and minimize the risk of unexpected breakdowns.
Moreover, predictive maintenance also contributes to workplace safety. According to a report, implementing maintenance in manufacturing could reduce[R4] safety, health, environmental and quality risks by, around 14% while extending the lifespan of aging assets by 20%. By detecting possible equipment failures beforehand companies can take preventive measures to avert accidents and injuries. This not ensures employee wellbeing but also helps in mitigating liability concerns and insurance expenses.
Key Components of Predictive Maintenance
Data Collection: The core of predictive maintenance is built upon the data collection. Real time information regarding the performance of equipment such as temperature, vibration, pressure and other factors is gathered by sensors, IoT devices and various monitoring tools. This data is subsequently sent to a system, for analysis.
Data Analysis: Advanced data analysis and machine learning algorithms are utilized to analyze the gathered data and detect patterns and irregularities. By examining data and deviations from standard operating conditions, these algorithms have the ability to forecast potential component failures. It is noteworthy that 79[R5] % of businesses perceive predictive maintenance as the primary use case, for industrial data analytics.
Condition Monitoring: Regularly assessing the health of equipment is crucial for condition monitoring. According to data, from 2018, 53[R6] % of facilities rely on a computerized maintenance management system (CMMS) to monitor their maintenance operations. Whenever anomalies are detected maintenance teams receive alerts enabling them to plan their maintenance activities.
How Predictive Maintenance Works
To establish a predictive maintenance program, it is crucial to follow a several steps. The initial phase involves collecting data, where information regarding the equipment’s temperature, vibration and other performance metrics is gathered. This data is then stored in a database, for analysis.
The subsequent step entails analyzing the collected data. Machine learning algorithms are employed to scrutinize the data and detect patterns that signify equipment failure. These algorithms also aid in predicting maintenance timing to prevent breakdowns.
IIoT-Enabled Asset Management And Asset Maintenance
The Industrial Internet of Things (IIoT) is one of the top driver for asset management in the industrial sector. It constitutes 17% of all global IoT initiatives. Nearly 98[R7] % of companies expect efficiency improvements by embracing digital technologies like integrated Manufacturing Execution Systems (MES), predictive maintenance or augmented reality solutions. It’s undeniable that asset management plays a role in every manufacturing operation. The IIoT ensures that productivity, efficiency and safety, in operations will consistently meet the expectations of both management and workers.
In the industrial sector, numerous manufacturing companies view asset management as a necessity. Despite their familiarity with adapting to evolving consumer demands and increasing costs they are determined to enhance their asset efficiency, for goal attainment.
Implementation Strategies:
Implementing a successful PdM program requires careful planning and execution. Some key steps include:
- Identifying critical assets: Prioritize assets with high downtime costs or criticality to overall operations.
- Installing sensors and collecting data: Implement sensors to collect real-time data on key parameters like vibration, temperature, and pressure.
- Building data infrastructure: Develop a robust data infrastructure to store, manage, and analyze collected data.
- Developing predictive models: Utilize machine learning and AI to develop models that predict potential equipment failures based on historical and real-time data.
- Integrate with existing systems: Integrate PdM with other relevant systems to ensure operational efficiency and facilitate data-driven decision-making.
Challenges of Predictive Maintenance
One of the obstacles that companies may encounter when implementing a predictive maintenance program is guaranteeing the quality of their data. Accurate and dependable data plays a role in making well informed decisions regarding equipment maintenance. It is imperative for companies to ensure that their data is current, comprehensive and free from any errors or biases.
Another challenge lies in facing resistance to change. Some employees may exhibit reluctance, in embracing technologies or processes particularly if they have been utilizing traditional maintenance strategies for an extended period of time. To tackle this challenge companies can provide training and support to assist employees in comprehending the advantages of maintenance and how it can enhance their work environment.
The challenges that need to be considered are:
- Data quality and security: Ensuring data quality and implementing robust cybersecurity measures is crucial for accurate predictions and system reliability.
- Technological expertise: Implementing and maintaining a PdM program requires expertise in data analysis, machine learning, and related technologies.
- Initial investment costs: The initial investment in hardware, software, and expertise can be substantial.
The Future of Predictive Maintenance (PdM):
As we step into the future, Predictive Maintenance is evolving rapidly, thanks to advancements in technology and data analysis. Here are some key trends that are shaping its landscape:
Teaming up with IoT: In the coming years, PdM is expected to become an integral part of broader Internet of Things (IoT) ecosystems. This integration will allow for real-time monitoring and optimization of interconnected assets, enhancing the overall efficiency of asset management.
Leveraging AI: The role of Artificial Intelligence (AI) and machine learning in PdM is set to become more significant. These technologies will improve the accuracy of predictive models, enabling them to detect complex failure patterns with higher precision. This means more reliable predictions and proactive maintenance strategies.
Embracing Automation: The future of PdM is also moving towards automation with the development of closed-loop systems. These systems can automatically trigger corrective actions when potential issues are identified, optimizing asset performance.
In a nutshell, the future of PdM is dynamic and technology-driven. The integration with IoT and advancements in AI signify a more interconnected and intelligent approach to asset management. The shift towards automated systems reflects a commitment to proactive and efficient maintenance strategies, heralding a new era in the optimization of asset performance.
Conclusion
Predictive maintenance is completely changing the way asset management works moving away from preventive methods and embracing a proactive and data driven strategy. This approach allows organizations to optimize their maintenance schedules cut down on costs and ensure reliability of their equipment.
With advancing technology its becoming increasingly essential for businesses in industries to adopt predictive maintenance as a competitive necessity. By embracing this transformative approach companies can gain insights, into their asset management practices and stay ahead in today’s ever changing business landscape.
[R1]https://www.upkeep.com/learning/maintenance-statistics/
[R2] [R2]https://www.plantengineering.com/articles/2018-maintenance-survey-playing-offense-and-defense/
[R3]https://webinarcare.com/best-predictive-maintenance-software/predictive-maintenance-statistics/#3
[R4]https://webinarcare.com/best-predictive-maintenance-software/predictive-maintenance-statistics/#3
[R5]https://webinarcare.com/best-predictive-maintenance-software/predictive-maintenance-statistics/#3
[R6]https://webinarcare.com/best-predictive-maintenance-software/predictive-maintenance-statistics/#3
[R7]https://marketsplash.com/industrial-internet-of-things-statistics/#link1
