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What Is Predictive Maintenance? How Can Predictive Maintenance Reduce Unplanned Machine Downtime?

Unexpected machine failures in production facilities can lead to lost output, high maintenance costs and disrupted schedules. In critical rotating equipment in particular, a small mechanical change that goes unnoticed can develop into a larger failure and an extended production shutdown.

Predictive maintenance is a maintenance approach that aims to detect early signs of potential faults by analysing machine operating data and planning maintenance activities according to the machine’s actual condition.

Unlike traditional maintenance methods, predictive maintenance does not rely solely on servicing equipment at fixed intervals or intervening after a failure. By monitoring machine health continuously or periodically, it enables earlier, data-driven decisions about equipment condition.

Modern predictive maintenance systems supported by AI, sensor technologies and digital twin solutions help industrial businesses make their maintenance processes more predictable and efficient.

What Is Predictive Maintenance?

Predictive maintenance is a strategy that aims to identify potential failures before they become serious problems by analysing a machine’s current operating condition and how it changes over time.

This approach evaluates machine data such as vibration, temperature, ultrasonic sound and magnetic flux . Changes in measurements can be compared with the machine’s normal operating behaviour to identify abnormal conditions.

For example, wear in an electric motor bearing can alter its vibration characteristics. Detecting this change in time can help the maintenance team carry out the necessary checks before the bearing fails completely and schedule an intervention at a suitable time.

The main purpose of predictive maintenance is not simply to detect faults. It also supports production continuity by providing insight into when maintenance may be needed.

Predictive maintenance therefore has important applications across production lines, electric motors, pumps, fans, compressors, gearboxes and other critical rotating equipment.

What Do the Turkish Terms for Predictive Maintenance Mean?

In industrial maintenance, the Turkish terms “kestirimci bakım” and “öngörücü bakım” are generally used to describe the same approach. Both correspond to the English concept of predictive maintenance.

In Türkiye, “kestirimci bakım” is widely used in technical publications and industrial applications, while “öngörücü bakım” is another term for the same concept.

The approach is based on monitoring machine data, analysing operating behaviour and assessing possible signs of faults.

Predictive maintenance systems can offer different capabilities depending on the technology used. Some identify abnormal vibration or temperature changes, while more advanced analytical models can use current data and historical trends to estimate failure risk or potential failure timing.

Predictive maintenance therefore involves not only collecting data from sensors, but also turning that data into meaningful maintenance decisions.

How Does Predictive Maintenance Work?

The predictive maintenance process begins with collecting machine health data. This data is analysed, deviations from normal operating behaviour are identified, and the findings are used in maintenance processes.

1. Collecting machine data

Sensors measure machine health indicators such as vibration, temperature, ultrasonic sound or magnetic flux. Measurement types are selected according to the equipment’s design and monitoring requirements.

2. Analysing the data

The collected data is evaluated to establish the machine’s normal operating behaviour. Comparisons are made with historical measurements to investigate unusual changes.

3. Identifying anomalies and possible signs of faults

Analytical methods and, where appropriate, AI models help identify patterns that may be associated with developing faults. Findings are evaluated alongside the equipment’s operating conditions and historical data.

4. Planning maintenance decisions

Based on the findings, maintenance teams can plan checks, interventions and work orders. Maintenance activities are therefore informed not only by the calendar, but also by data about the machine’s actual condition.

The effectiveness of this process depends on measurement quality, the choice of appropriate analysis methods, equipment operating conditions and expert assessment of the findings.

Collected data helps maintenance teams plan checks and interventions.

Predictive Maintenance vs. Preventive Maintenance

Preventive maintenance and predictive maintenance are two different approaches aimed at reducing unplanned downtime. The main difference is the criterion used to decide when maintenance should take place.

Maintenance approach How does it work? Main limitation Reactive maintenance Intervention takes place when equipment fails. Carries a risk of unplanned downtime and lost production. Preventive maintenance Maintenance takes place at fixed time or operating-hour intervals. May result in early or unnecessary maintenance, regardless of machine condition. Predictive maintenance Machine data and health indicators are evaluated to determine maintenance needs. Requires reliable data and appropriate analysis.

For example, servicing an electric motor every six months is preventive maintenance. Regularly analysing the same motor’s vibration data and planning maintenance based on signs of bearing wear is predictive maintenance.

Predictive maintenance does not have to eliminate preventive maintenance entirely. The two approaches can be combined to create a more comprehensive strategy based on equipment criticality, production requirements and maintenance policies.

What Are the Benefits of Predictive Maintenance?

The benefits of predictive maintenance vary according to a business’s equipment, failure history and implementation scope. When properly planned, its main advantages include:

1. Helping reduce unplanned machine downtime

Early identification of changes in machine health supports timely intervention in problems that could develop into failures. This can help reduce unexpected shutdowns and disruptions to production plans.

2. Optimising maintenance costs

Data about the actual condition of equipment helps prioritise maintenance activities. Resources can be directed towards more critical equipment, with the aim of reducing unnecessary interventions. For factors that influence return on investment, see this article for more details.

3. Supporting equipment lifespan

Identifying mechanical problems such as wear, imbalance or bearing issues at an early stage can provide time for appropriate intervention. Timely maintenance can help prevent secondary damage and support more efficient use of equipment.

4. Supporting production continuity

Assessing maintenance needs in advance makes it easier to schedule interventions around production plans. This can improve coordination between maintenance activities and production targets.

5. Strengthening maintenance teams’ decision-making

Data-driven monitoring helps maintenance teams assess equipment condition without relying solely on operator reports or periodic inspections. Historical machine health data can also be used to investigate recurring causes of faults.

6. Potentially contributing to improved energy efficiency

Some mechanical problems can reduce equipment operating efficiency. Monitoring indicators such as vibration and temperature supports investigation of performance changes. Energy savings depend on the nature of the problem identified and the improvements made.

What Is AI-Powered Predictive Maintenance?

AI-powered predictive maintenance involves evaluating machine health data using advanced analytical methods and suitable machine learning models.

Traditional condition monitoring systems may generate alerts when predefined thresholds are exceeded. When sufficient, suitable data is available, AI-powered systems can offer broader assessments by analysing relationships between variables and patterns over time.

For example, a change in a motor’s vibration level may not provide enough information on its own. Analysing vibration data alongside operating conditions, temperature and historical measurements can help explain abnormal behaviour more clearly.

It is important to distinguish between three concepts:

Condition monitoring: Tracking data about the equipment’s current condition.

Anomaly detection: Identifying deviations from the machine’s expected operating behaviour.

Failure prediction: Estimating possible failures or remaining time to failure using data and suitable models.

These capabilities are not available to the same extent in every system. Detecting an anomaly does not, on its own, mean that a specific component will definitely fail or indicate when a failure will occur. Results need to be considered alongside equipment knowledge and expert assessment.

How Is Digital Twin Technology Used in Predictive Maintenance?

A digital twin is a model that represents the characteristics and behaviour of a physical asset or system in a digital environment. In industrial applications, digital twin technology can be used to analyse equipment data and better understand system behaviour.

In predictive maintenance, a digital twin approach can support assessment of a machine’s current condition, review of its historical behaviour and, in suitable scenarios, analysis of different operating conditions.

A digital twin represents the behaviour of physical equipment in a digital environment.

For example, when operating data from critical motors in a production facility is tracked digitally, maintenance teams can compare equipment health, examine changes and set maintenance priorities.

However, digital twins, machine health monitoring and predictive maintenance are not interchangeable concepts. A digital twin is an approach to digital representation and analysis; predictive maintenance is a strategy for planning maintenance decisions based on equipment condition and potential signs of faults. A digital twin application’s ability to predict failures depends on its underlying models and data infrastructure.

Why Is Machine Health Monitoring Important for Rotating Equipment?

Rotating equipment such as electric motors, pumps, fans, compressors and gearboxes plays a critical role in production processes across many industrial facilities . Mechanical problems in this equipment can also affect the systems connected to it.

The main data types used in machine health monitoring include:

Vibration: Can help identify changes associated with imbalance, misalignment and certain bearing or gear problems.

Ultrasonic sound and acoustic emission: Depending on the equipment and measurement method, these can be used to investigate friction, impacts or certain signs of mechanical deterioration. Learn more about acoustic emission .

Magnetic flux: Can provide information about changes in electrical or electromagnetic behaviour under suitable motor and measurement conditions. Fault detection using magnetic flux .

Temperature: Can contribute to assessment of overheating and changes in operating conditions.

No single measurement type is sufficient to identify every type of fault. The monitoring approach should therefore be designed around the equipment’s characteristics and potential failure modes.

Regular monitoring of machine health data helps make the condition of critical equipment more visible and supports prioritisation of maintenance resources.

Turn Predictive Maintenance into Practice with Fizix

Putting the approach described above into practice requires accurate measurements, a regular flow of data and a way to make that data understandable and useful for maintenance teams.

Fizix offers a machine health monitoring solution that uses AI and digital twin technologies to monitor the health of rotating equipment and support better-informed maintenance decisions.

The Fizix ecosystem brings together data from different sensor technologies and combines it on the MATE digital platform to support monitoring of equipment operating conditions and evaluation of health indicators.

Fizix solutions support:

Continuous machine health monitoring: Measurements from equipment are tracked to make changes in machine condition visible.

Data-driven analysis: Machine health indicators are evaluated to support investigation of abnormal conditions.

Monitoring through a digital platform: Equipment data, health indicators and related analyses can be viewed in a central environment.

Support for maintenance processes: The findings help maintenance teams assess and prioritise interventions.

This approach can make machine health monitoring more systematic, especially in facilities with large numbers of rotating assets. For a real-world example, read our cement industry deployment story for a closer look.

The value predictive maintenance brings to a business depends on the equipment monitored, measurement technologies, data coverage and integration with maintenance processes. The aim is not merely to collect data, but to turn it into meaningful decisions that support production continuity.

Frequently Asked Questions

Do the Turkish terms “kestirimci bakım” and “öngörücü bakım” mean the same thing?

Yes. Both terms generally refer to the same approach, known in English as predictive maintenance. In Turkish technical publications, “kestirimci bakım” is more commonly used.

What data is used in predictive maintenance?

Depending on the equipment and monitoring requirements, machine health indicators include vibration, temperature, ultrasonic sound, acoustic emission and magnetic flux. No single measurement type is sufficient to identify every type of fault.

Does predictive maintenance replace preventive maintenance?

Not necessarily. The two approaches can be combined to create a more comprehensive maintenance strategy based on equipment criticality, production requirements and maintenance policies.

Does detecting an anomaly mean the machine will fail?

No. Anomaly detection identifies deviations from a machine’s expected behaviour; on its own, it does not mean that a specific component will definitely fail or indicate when a failure will occur. Findings should be considered alongside equipment knowledge and expert assessment.

Which types of equipment are suitable for predictive maintenance?

Electric motors, pumps, fans, compressors, gearboxes and other critical rotating equipment are among the main applications.

Explore Fizix solutions to monitor your machines’ health more closely and develop your maintenance processes with a data-driven approach.

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