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Types of Predictive Maintenance

Key Takeaways

  • Predictive maintenance is a crucial solution in technology-driven industries, utilizing advanced technologies and data analytics to predict equipment failures.

  • Different types of predictive maintenance subtypes include corrective, preventive, risk-based, condition-based, and predetermined maintenance, each serving unique purposes in maintaining equipment reliability.

  • Types of predictive maintenance technologies encompass a range of tools such as oil analysis, time domain reflectometry, vibration analysis, spectroscopy, proximity probes, and performance trending, each contributing to the optimization of equipment performance

predictive maintenance used to detect equipment malfunction

There are multiple methods of classifying the various types of predictive maintenance.

In today's technology-driven world, industries across the spectrum face significant challenges to maintain the reliability and efficiency of their machinery and equipment. Downtime and unexpected failures can be costly, leading to production delays, increased maintenance costs, and reduced profits. Predictive maintenance (PdM) has emerged as a solution, leveraging advanced technologies and data analytics to predict equipment failures before they occur, enabling proactive maintenance strategies. Read on as we discuss the various types of predictive maintenance that are transforming industries and shaping the future of reliability.

Type of Predictive Maintenance


Corrective Maintenance

Aims to restore equipment functionality when a fault is detected, whether planned or unplanned, potentially incurring unforeseen costs for unplanned repairs.

Preventative Maintenance

Proactively reduces equipment breakdowns by following a predefined schedule and analyzing past data, extending equipment lifespan and ensuring sustained productivity.

Risk-Based Maintenance

Efficiently allocates resources to minimize risks, enhancing cost-effectiveness and overall operational efficiency while safeguarding critical equipment.

Condition-Based Maintenance

Relies on real-time sensor data to trigger maintenance actions only when equipment performance declines, offering long-term cost savings but requiring regular monitoring.

Predetermined Maintenance

Relies on manufacturer-provided programs and historical data but lacks the precision of condition-based maintenance, potentially leading to equipment breakdowns.

Types of Predictive Maintenance

When discussing types of predictive maintenance, the concept encompasses various meanings. For instance, there can be different subtypes of predictive maintenance, which are distinct approaches to foreseeing equipment failures. Another facet of predictive maintenance can involve different types of predictive maintenance technologies. Beyond predictive maintenance, there are other types of maintenance as well. We’ll be touching on all three.

The first method of categorizing "types of predictive maintenance" refers to the various subtypes that collectively constitute predictive maintenance.

  • Corrective maintenance comes into play when a fault is detected in equipment. Its primary goal is to restore the equipment's functionality and performance to the desired level. Corrective maintenance can be planned or unplanned, depending on whether a maintenance plan existed before the failure occurred. Unplanned corrective maintenance may be costly due to unforeseen expenses, whereas planned corrective maintenance is more cost-effective as it is part of a pre-arranged maintenance schedule.

  • Preventive maintenance focuses on reducing the likelihood of equipment breakdowns by preventing potential issues from arising. Maintenance teams analyze historical data and past failures to establish well-structured maintenance programs. By doing so, they can identify potential failure points and take preventive actions to mitigate risks. Preventive maintenance is considered planned as it follows a predefined maintenance schedule, which can be efficiently managed through a Computerized Maintenance Management System (CMMS). Implementing preventive maintenance ensures prolonged equipment lifespan and sustained productivity.

  • Risk-based maintenance aims to address risk-sensitive systems and machinery. Its primary focus is on efficiently allocating resources to minimize or repair risks to ensure the safety of employees, employers, and company assets. RBM provides cost-effective strategies to safeguard critical equipment and systems. By identifying vulnerable components and implementing appropriate maintenance measures, RBM significantly impacts a company's cost-effectiveness and overall operational efficiency.

  • Condition-based maintenance is a type of predictive maintenance that relies on sensor data, such as vibration monitoring systems, to assess the condition of equipment while it is in operation. Maintenance actions are only triggered when the predictive maintenance dataset indicates a decline in equipment performance. CBM is a sophisticated approach, requiring regular monitoring and check-ups to detect deviations in real time. Although more complex, CBM acts as a preventive measure, reducing the likelihood of failures and enabling companies to save significant costs in the long run.

  • Predetermined maintenance is the least popular predictive maintenance method, as it relies solely on the maintenance programs provided by equipment manufacturers rather than monitoring the actual equipment condition. These programs are based on the manufacturers' knowledge of failure mechanisms and past MTTF (mean time to failure) data. The effectiveness of predetermined maintenance depends on the accuracy of the manufacturer's program and assumptions made about the equipment's failure risks. However, it lacks the precision of condition-based maintenance and may not fully prevent equipment breakdowns.

Types of Predictive Maintenance Technologies

Another approach to categorizing predictive maintenance involves focusing on predictive maintenance technologies. While we have already extensively covered predictive maintenance technologies for the electronics industry and the automotive industry, we will now delve deeper into other types of predictive maintenance technologies beyond the electronics industry.

  • Oil analysis: Lubrication plays a critical role in the performance and longevity of mechanical equipment. Oil analysis involves regular sampling and testing of lubricants to monitor the presence of contaminants, degradation, and wear particles. Unusual oil properties can reveal problems like bearing wear, gear damage, and fluid contamination. By interpreting oil analysis results, maintenance teams can optimize lubrication practices and predict equipment failures, extending the lifespan of critical assets.

  • Time domain reflectometry (TDR): Time Domain Reflectometry (TDR) is a predictive maintenance technique used to diagnose issues in cables and transmission lines. By sending electrical pulses along the cable and measuring the reflection of these pulses, TDR can accurately locate cable faults, such as open circuits and shorts. This method helps maintenance teams quickly identify and repair cable problems, minimizing system downtime and disruptions.

  • Vibration analysis: This is the go-to type of analysis for predictive maintenance inside manufacturing plants with high-rotating machinery. Because it’s been around longer than other types of condition monitoring, it’s relatively cost-effective. In addition to detecting looseness like in the example above, vibration analysis can also discover imbalance, misalignment, and bearing wear. Vibration analysis is one of the most established and widely used predictive maintenance techniques. 

  • Spectroscopy is a type of predictive maintenance technique used for analyzing the chemical composition of lubricants and fluids used in machinery. By studying the spectral characteristics of the lubricants, maintenance teams can identify the presence of wear particles, contaminants, and degradation byproducts. Spectroscopic analysis can pinpoint potential issues at an early stage, allowing for timely lubricant replacement and maintenance interventions.

  • Proximity probes, also known as displacement probes, are used to monitor the gap or distance between rotating components, such as shafts and bearings, in rotating machinery. These probes detect changes in the gap, indicating wear, misalignment, or imbalance. By continuously monitoring the gap, maintenance teams can take corrective actions before the problems escalate.

  • Tribology monitoring is the study of friction, wear, and lubrication of interacting surfaces in machinery. It involves analyzing factors such as surface roughness, lubricant properties, and contact stress to assess the health of equipment components. By understanding the tribological behavior, maintenance teams can optimize lubrication practices, identify potential wear-related problems, and make informed decisions about maintenance intervals. 

  • Performance trending is a data-driven predictive maintenance technique that involves tracking and analyzing equipment performance data over time. This technique helps identify trends and deviations from normal operating behavior. Performance trending can include parameters such as temperature, pressure, flow rates, and other critical operating variables. Early identification of performance anomalies enables proactive maintenance actions, reducing the likelihood of unexpected failures and optimizing equipment performance.

  • Other previously discussed types of predictive maintenance technologies include acoustic monitoring, temperature monitoring, vibration analysis, motor circuit analysis, ultrasonic testing systems, current signature analysis, and acoustic emission testing.

Just as the various types of predictive maintenance all work together to create more reliable and safe machines, so does Allegro X for integrated circuits. By enabling efficient collaboration between chip and package designers, Allegro X Advanced Package Designer ensures optimized physical layouts for single- and multi-die packages, aligning with the preventive approach of predictive maintenance. The software's constraint-driven design, real-time DRC, and integrated analysis technologies seamlessly contribute to identifying potential issues, enhancing reliability, and mitigating risks. 

Leading electronics providers rely on Cadence products to optimize power, space, and energy needs for a wide variety of market applications. To learn more about our innovative solutions, talk to our team of experts.