What's the Difference Between Preventive and Predictive Maintenance? Choosing the Optimal Maintenance Strategy Based on Purpose, Methods, and Applications

Maintaining stable production systems is an urgent challenge in modern manufacturing. Equipment failures can cause significant problems, including production stoppages, increased costs, and safety risks. To mitigate these risks, planned and efficient equipment maintenance is essential.

Traditional “reactive maintenance” often left companies scrambling to address sudden troubles, frequently disrupting production schedules. This has led to increased attention on concepts like “preventive maintenance” and “predictive maintenance.”

Here, for companies aiming for stable factory operation, we introduce the differences between maintenance methods, how to proceed with different approaches based on technique, and how to select appropriate equipment maintenance strategies by purpose.

Selecting the Optimal Maintenance Method Based on Purpose

For stable factory operation, understanding the characteristics of each maintenance method and selecting the optimal one based on purpose is crucial. Effective maintenance strategies are key to efficient equipment management.

Selecting Corrective Maintenance Considering Cost and Risk

Corrective maintenance is the most basic maintenance approach, involving repairs only after equipment fails. While its purpose is to restore functionality post-failure, challenges include production schedule delays due to unexpected stoppages and high emergency repair costs.

This approach is limited to equipment where failure has minimal impact on production or to components where repair costs are low. It is not recommended for major production equipment or equipment where failure poses safety risks. While requiring minimal initial investment, it must be chosen with an understanding that it carries significant unexpected risks.

Selecting Preventive Maintenance: Balancing Planning and Efficiency

Preventive maintenance is a concept that involves planning and performing inspections and repairs before equipment fails, aiming to prevent unexpected breakdowns and ensure stable operation. It includes two main approaches.

Time-Based Maintenance

This method involves performing inspections and part replacements at regular intervals based on operating hours or time periods. It is suitable for equipment where part lifespans are predictable, where failure would cause a significant impact, or where regulations mandate regular inspections. While it allows for easy planned management, there is also a risk of over-maintenance.

Condition-Based Maintenance

This method continuously monitors the actual condition of the equipment and determines the necessity for maintenance based on the data. It enables real-time health assessment and reduces unnecessary maintenance. Condition-based maintenance, also known as “predictive maintenance,” has garnered significant attention and evolved in recent years.

Choosing Predictive Maintenance for Risk Avoidance and Optimization

As the most advanced form of condition-based maintenance, predictive maintenance utilizes sophisticated sensor technology and data analysis to accurately predict failure precursors and perform maintenance at the optimal time. It is ideal for critical equipment where production line stoppages are unacceptable or for equipment with high repair costs.

Choosing predictive maintenance enables maintenance “only what is needed, when it is needed, and only as much as is needed.” This avoids production stoppages, minimizes costs, and maximizes uptime. Predictive maintenance represents the ideal maintenance approach modern manufacturing should strive for.

Different Maintenance Approaches by Method

Among preventive maintenance methods, time-based maintenance and condition-based maintenance (also known as predictive maintenance) each employ distinct approaches and technologies.

Specific Methods and Application Examples of Time-Based Maintenance

Time-based maintenance involves systematically performing inspections and part replacements based on predetermined time intervals or operating hours. The approach is relatively straightforward: inspection and replacement schedules are determined based on manufacturer recommendations or historical data, and then executed according to the schedule. Examples include regular oil changes and filter replacements.

Time-based maintenance is suitable for equipment or parts with relatively stable component lifespans and simple failure modes. It offers high predictability and facilitates personnel and parts scheduling. However, risks include over-maintenance (replacing still-functional parts) and unexpected failures occurring between scheduled inspections. Continuous reassessment of appropriate replacement timing is crucial.

Specific Methods and Technologies for Predictive Maintenance (Condition-Based Maintenance)

Predictive maintenance is a method that continuously monitors the actual condition of equipment to detect signs of failure in advance, enabling maintenance at the optimal time. Its specific approach involves the combined use of the following technologies:

Data Collection via Sensors

The foundation of predictive maintenance is the real-time collection of data indicating equipment abnormalities. Selecting and placing sensors appropriate for the application enables more accurate condition monitoring. Vibration sensors detect minute vibration changes in rotating machinery with high precision, temperature sensors identify overheating or lubrication issues, and current sensors detect motor load abnormalities. Recent adoption of wireless sensors (including Wi-Fi) has enabled easier installation and data collection over wider areas.

Data Analysis (AI/Machine Learning) and Diagnosis

The vast amount of collected data is analyzed using advanced analytical software, complementing established diagnostic techniques. AI and machine learning are leveraged to learn normal and abnormal patterns. This enables the automatic identification of subtle changes and complex correlations that are difficult for humans to detect, predicting the type of failure, its progression, and even the remaining life of the equipment. The system presents diagnostic results clearly, supporting rapid decision-making.

Reflecting Findings in Maintenance Plans and Execution

Information on impending failures obtained through predictive maintenance is incorporated into specific maintenance plans. For equipment where failure is predicted, planned parts procurement and repair work are carried out before the problem worsens. This avoids sudden production stoppages, improves work efficiency, and maximizes overall plant uptime.

Selecting Appropriate Equipment Maintenance Strategies by Purpose

By selecting the optimal maintenance strategy tailored to a company's specific challenges and objectives, equipment management efficiency can be optimized and competitiveness enhanced.

Maintenance Strategies for Cost Reduction Focus

If cost reduction is the top priority in equipment management, predictive maintenance is the most effective strategy. By using sensors and data analysis to detect early signs of failure and perform maintenance at the optimal time, it eliminates waste from unnecessary part replacements and over-maintenance. This significantly reduces opportunity losses from unplanned stoppages and emergency repair costs, contributing to overall cost optimization.

Maintenance Strategy for Prioritizing Uptime Improvement and Productivity Stability

Maximizing productivity and stabilizing up time are among the most critical challenges in the manufacturing industry. Predictive maintenance truly shines in achieving these goals. Corrective maintenance takes time to restore operations after a failure occurs, halting the production line, while time-based maintenance also involves scheduled shutdowns.

Predictive maintenance minimizes the risk of sudden production stoppages by enabling the early detection of failure signs and allowing for planned maintenance before problems escalate. Maintenance work can also be scheduled during times that minimally impact production plans, preventing overall uptime reduction. This improves production planning accuracy and significantly contributes to maintaining a stable production system.

Maintenance Solutions Addressing Labor Shortages and Skill Transfer

As the aging of skilled technicians and labor shortages intensify across the manufacturing industry, transferring equipment maintenance knowledge and skills has become an urgent challenge. Predictive maintenance also serves as an effective solution for this issue.

By leveraging automatic sensor measurements and data analysis, it significantly reduces the frequency of manual periodic patrols and visual inspections. This enables a smaller workforce to manage more equipment, contributing to alleviating labor shortages. The diagnostic results provided by the system make it easier for personnel without specialized knowledge to identify equipment abnormalities, preventing judgments from becoming overly dependent on individual expertise. By basing some decisions on data rather than relying solely on the experience and intuition of skilled technicians, the burden of knowledge transfer is reduced, enabling both efficient maintenance operations and consistent quality control.

The Wi-Fi Vibration Sensor “conanair” contributes to the maintenance of factory equipment！

NSXe offers its self-developed product, the “Wi-Fi Vibration Sensor,” which was developed in response to feedback from users of vibration sensors and maintenance personnel. This sensor enables condition monitoring using vibration data at a low cost of approximately ¥30,000. It connects to tablets or smartphones via a browser, eliminating the need for special apps or software, making it easy for anyone to use. A single unit can measure vibrations at multiple locations (Manual Mode). When used in conjunction with the included app for automated measurement across multiple conanair units, it enables the construction of IoT systems that utilize the cloud and AI-based anomaly detection systems.

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