Matsushima Measure Tech
Facility occupancy rate and operation efficiency is very important factor for competitiveness of production factories, and so, unexpected troubles or failures of facility which may cause delivery delay or expensive repairing cost becomes serious risk.
Then nowadays many factories take both Predictive Maintenance and Preventive Maintenance strategies to prevent those risks.
This article shows concepts of predictive maintenance and preventive maintenance, difference between them and each advantages in the following.
Then nowadays many factories take both Predictive Maintenance and Preventive Maintenance strategies to prevent those risks.
This article shows concepts of predictive maintenance and preventive maintenance, difference between them and each advantages in the following.
What is Preventive Maintenance?
Preventive maintenance is a kind of maintenance strategy focusing on achieving periodical inspection or maintenance before failure happens on facility.
Preventive maintenance schedule is generally planned based on past failure records or facility/machine manufacturer's recommendation.
It is effective to reduce failure risk but not optimum way because actual facility situation is not monitored or considered.
Preventive maintenance schedule is generally planned based on past failure records or facility/machine manufacturer's recommendation.
It is effective to reduce failure risk but not optimum way because actual facility situation is not monitored or considered.
What is Predictive Maintenance?
Predictive maintenance is a kind of maintenance strategy focusing on monitoring actual situation of facility and predicting future failure risk.
It detects the sign of abnormality of future failure by achieving real time monitoring of machine situation with sensors or data analyzing tools.
It is possible to take countermeasures before failure happens and prevent unexpected sudden facility stop and then maximize the lifetime and occupation rate of facility.
It detects the sign of abnormality of future failure by achieving real time monitoring of machine situation with sensors or data analyzing tools.
It is possible to take countermeasures before failure happens and prevent unexpected sudden facility stop and then maximize the lifetime and occupation rate of facility.
Difference between Predictive Maintenance and Preventive Maintenance
Main difference is basis of maintenance plan.
Basis of Preventive maintenance is past experience or records but the basis of Predictive maintenance is real time actual situation or performance data of facility.
It can reduce unnecessary maintenance work improve productivity in case of Predictive maintenance because it is possible to make more precise maintenance plan by utilizing technology and data.
Basis of Preventive maintenance is past experience or records but the basis of Predictive maintenance is real time actual situation or performance data of facility.
It can reduce unnecessary maintenance work improve productivity in case of Predictive maintenance because it is possible to make more precise maintenance plan by utilizing technology and data.
Advantages of Preventive maintenance
* Reduce risk of unexpected failure by regular maintenance
* Keep stable facility performance
* Prevent expensive repair cost of facility by failure
* Keep stable facility performance
* Prevent expensive repair cost of facility by failure
Advantages of Predictive maintenance
* Reduce facility downtime by future failure prediction
* Improve maintenance planning and optimize those work
* Reduce total production cost by facility lifetime extension
* Improve productivity and quality control
* Improve maintenance planning and optimize those work
* Reduce total production cost by facility lifetime extension
* Improve productivity and quality control
An example of successful case by predictive maintenance
There are many industrial dust collectors in factories for material industry or process industry because those factories handles solids or powders in many processes.
However, there is a serious risk to spread dust particles when dust collector has a trouble.
This type of trouble like sudden leakage from the dust collector may happen even if periodical inspection is achieved.
Continuous monitoring is necessary to solve this problem but visual observation by human is not efficient and realistic.
Matsushima Measure Tech offers DUST MONITOR to help your Predictive Maintenance to solve this problem.
DUST MONITOR provides real time continuous monitoring for dust particles concentration leaked from dust collector and possible to give an alarm before dust leakage comes over the reference limit.
For more details, please refer to the following information.
However, there is a serious risk to spread dust particles when dust collector has a trouble.
This type of trouble like sudden leakage from the dust collector may happen even if periodical inspection is achieved.
Continuous monitoring is necessary to solve this problem but visual observation by human is not efficient and realistic.
Matsushima Measure Tech offers DUST MONITOR to help your Predictive Maintenance to solve this problem.
DUST MONITOR provides real time continuous monitoring for dust particles concentration leaked from dust collector and possible to give an alarm before dust leakage comes over the reference limit.
For more details, please refer to the following information.
Reference Articles
Other case studies
Oil and Gas Industry Implementation
The oil and gas sector has demonstrated significant success with predictive maintenance implementation, particularly in preventing equipment failures and reducing operational downtime. Leading companies in this industry have implemented comprehensive predictive maintenance programs using digital workflow builders and AI-powered analytics to anticipate equipment failures and optimize maintenance scheduling. These implementations have resulted in substantial cost savings and improved operational efficiency through proactive maintenance strategies.
Manufacturing Sector Applications
The manufacturing industry has embraced predictive maintenance as a means to optimize production line efficiency and reduce unexpected breakdowns. Automobile manufacturers have successfully integrated predictive maintenance workflows to predict potential equipment failures and schedule maintenance activities accordingly. This proactive approach has resulted in reduced unexpected breakdowns, improved production efficiency, and significant cost savings through optimized maintenance timing.
Energy and Power Generation
Power generation facilities have implemented predictive maintenance to enhance reliability and efficiency while reducing maintenance costs. Leading power companies utilize AI-powered predictive maintenance workflows to monitor critical equipment such as turbines, generators, and transmission systems. These implementations enable proactive maintenance scheduling, significant downtime reduction, and improved plant reliability.
The energy sector benefits particularly from predictive maintenance due to the critical nature of power generation equipment and the high costs associated with unplanned outages. Condition monitoring techniques including thermal analysis, vibration monitoring, and oil analysis provide comprehensive insights into equipment health, enabling maintenance teams to address potential issues before they impact power generation capacity.
Conclusion
Predictive maintenance represents a transformative approach to asset management that leverages advanced technologies to optimize maintenance strategies and enhance operational performance. The comprehensive analysis of current implementations, technological architectures, and industry experiences demonstrates the significant potential for predictive maintenance to deliver substantial return on investment while improving safety, reliability, and operational efficiency. Organizations that successfully implement predictive maintenance programs can achieve maintenance cost reductions of up to 30%, improve asset utilization by 20%, and extend equipment lifecycles through optimized maintenance timing.
The key to successful predictive maintenance implementation lies in addressing both technological and organizational challenges through systematic deployment strategies that consider cultural readiness, data capabilities, and strategic alignment. While initial investments in sensor technology, analytical infrastructure, and personnel training can be substantial, the long-term benefits justify these expenditures through reduced downtime, improved safety, and enhanced asset performance. As artificial intelligence capabilities continue to advance and Industry 4.0 technologies mature, predictive maintenance will evolve toward more autonomous and intelligent systems that further optimize maintenance strategies and operational outcomes. Organizations that embrace these technologies and develop the necessary organizational capabilities will gain significant competitive advantages in an increasingly complex and demanding industrial environment.
The key to successful predictive maintenance implementation lies in addressing both technological and organizational challenges through systematic deployment strategies that consider cultural readiness, data capabilities, and strategic alignment. While initial investments in sensor technology, analytical infrastructure, and personnel training can be substantial, the long-term benefits justify these expenditures through reduced downtime, improved safety, and enhanced asset performance. As artificial intelligence capabilities continue to advance and Industry 4.0 technologies mature, predictive maintenance will evolve toward more autonomous and intelligent systems that further optimize maintenance strategies and operational outcomes. Organizations that embrace these technologies and develop the necessary organizational capabilities will gain significant competitive advantages in an increasingly complex and demanding industrial environment.
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