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Predictive maintenance is an innovative maintenance strategy aimed at identifying problems early and operating assets more efficiently. By using technologies such as Machine Learning (ML), Artificial Intelligence (AI), and IoT sensors (Internet of Things), this method analyzes real-time data to predict potential failures based on algorithms and historical data.   

The goal is to detect issues before they occur in order to avoid unplanned downtime and extend the lifespan of assets. At the same time, costs can be reduced compared to predictive maintenance, as predictive maintenance only triggers maintenance activities when they are actually necessary. This eliminates unnecessary tasks such as inspections or routine maintenance. Especially for complex systems, this leads to significant cost savings. 

What are the advantages of predictive maintenance? 

Unplanned downtimes and unexpected failures are among the most complex and costly challenges in asset-intensive industries such as manufacturing and energy. In addition to traditional maintenance strategies like reactive or preventive maintenance, predictive maintenance offers effective solutions to address these issues. 

When combined with Enterprise Asset Management (EAM), it provides precise insights into asset conditions and enables timely planning and execution of maintenance tasks before disruptions occur. It helps organizations shift from reactive and hard-to-plan maintenance processes to a more proactive and predictable strategy. 

The key advantages of predictive maintenance can be summarized as follows: 

• Minimized Downtime: In practice, issues are often only detected once they’ve already occurred. Predictive maintenance helps identify potential problems early on, effectively preventing unplanned failures. 
• Optimized Resource Usage: Traditional preventive strategies rely on routine activities to prevent issues through regular care and inspection. Predictive maintenance, on the other hand, triggers maintenance only when needed — saving both time and costs. 
• Extended Asset Lifespan: Proactive monitoring and the early planning of targeted interventions reduce wear and damage, protecting equipment throughout its lifecycle. 

How does predictive maintenance work? 

Predictive maintenance combines various technologies and data sources to detect issues early or to predict the optimal time for maintenance activities. Different methods are used — ranging from simple anomaly detection to the identification and categorization of specific failure states, and even the precise prediction of the ideal maintenance timing. 

Depending on the use case, the required data and preparations can vary significantly — from simple implementations to complex, time-consuming processes. The following outlines the general steps involved. 

The process involves several steps: 

1. Collect and prepare data: Historical data provides the foundation for training machine learning models. It is therefore necessary to gather data from various sources, including IoT sensors as well as operational data and maintenance records. The volume and quality of data — for example, from a group of similar assets — are critical to the accuracy of the predictions. 
2. Categorize failure states: Before machine learning models can be trained, failure states must not only be identified but also systematically categorized. By analyzing failure patterns and operational data, anomalies can be assigned to specific fault categories. This enables more accurate root cause analysis, better prioritization of maintenance actions, and more effective repair planning. 
3. Train machine learning models: Algorithms learn from data patterns to identify problems early. Machine learning models — such as anomaly detection algorithms capable of recognizing deviations — are trained based on this data. They learn to distinguish between normal and faulty operating conditions. 
4. Analyze real-time data: Sensors transmit machine parameters such as temperature, vibration, or pressure in real time while the CMMS system evaluates them using the trained machine learning models. Deviations from normal values can thus be detected early, allowing targeted actions to be initiated. 
5. Derive maintenance actions: When machine learning or other algorithms suggest maintenance actions, alerts are automatically triggered. Maintenance planners can then initiate follow-up actions, such as scheduling a work order. 

Data Sources and Their Importance 

Various data sources are used in predictive maintenance to train models and make informed decisions: 

• Failure, Repair, and Maintenance Data: 
  Historical information on past failures or repairs, as well as historical sensor data (time series), is essential for identifying patterns. This enables frequent issues to be recognized and addressed proactively. For example, in order to predict failures, it is important to collect both sensor data and records of problems and downtimes. This allows the AI to learn these failure states and predict them using live data (sensor readings) before they occur. 
• Operational Data: 
  Real-time data from sensors shows how equipment behavior changes during operation. These data points are also used to train machine learning models or to apply statistical algorithms for forecasting (e.g., linear regression). Different algorithms require different types of data — for anomaly detection, it is especially useful to collect large amounts of data from normal operation to model the baseline state. 
• Metadata: 
  Static information such as model number, technical specifications, or year of manufacture provides additional context that can improve prediction accuracy. Similar assets within an organization can be grouped together to increase data volume across different configurations for training machine learning models. 

Examples for predictive maintenance 

Predictive maintenance is particularly suitable for: 

• Critical assets: When failure involves high costs or safety risks. This is especially true for critical equipment that business operations depend on (e.g., air purification systems in pharmaceutical production, transformer stations in energy supply, etc.). 
• Predictable issues: Assets with clear wear patterns or known failure causes particularly benefit from predictive maintenance. 
• Data availability: When sensors and real-time monitoring systems are already in place (e.g., existing SCADA systems, easily retrofittable sensors, or existing production control systems with built-in sensors). 

In less critical areas, where failures are rare and easily scheduled, a condition-based or reactive maintenance strategy may be more cost-effective, as the effort involved in implementing PdM should not be underestimated. 

How does maintenance software support predictive maintenance? 

A modern maintenance software solution, such as the osapiens CMMS, simplifies the implementation of predictive maintenance through the following features: 

• Data Integration: Automatic collection and processing of data from various sources: management of assets and sensors, recording of malfunctions and related data, processing of sensor data, and automatic creation of follow-up actions. 
• Automated Analytics: Machine learning models detect anomalies early and automatically generate maintenance notifications, which can be reviewed and processed directly by a maintenance planner. 
• Efficient Planning: Maintenance activities are prioritized based on asset criticality and can be handled alongside reactive or predictive maintenance tasks. 

Conclusion 

Predictive maintenance is an effective strategy for maximizing asset availability, reducing costs, and increasing operational safety. By leveraging modern technologies and data-driven approaches, companies can take their maintenance strategies to the next level. With the support of powerful maintenance software, the benefits of predictive maintenance can be fully realized — resulting in greater efficiency, reduced downtime, and lower costs.