Did you know that a CMMS can help you start using predictive maintenance techniques in your industrial company? Read on to learn about this type of maintenance, how it has evolved over time, and its main advantages.
In today’s industrial environment, increasingly focused on gaining efficiency and productivity, predictive maintenance is emerging as an essential tool to anticipate equipment failures, reduce downtime, and optimize resource use.
In this article, we explain what predictive maintenance is and outline its four maturity levels. We also explore how a CMMS like MapexMM can help you bring this proactive approach into your factory by leveraging data analysis and advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), and machine learning (ML).
What is predictive maintenance?
Predictive maintenance is a maintenance strategy focused on anticipating and preventing equipment failures before they occur, enabling a much more efficient and precise management of resources.
Unlike corrective maintenance, which reacts to breakdowns, or preventive maintenance, which follows a set intervention schedule, predictive maintenance uses real-time data from equipment and key variables, such as temperature, vibration, or pressure, to identify potential problems.
Technology plays a crucial role in this process: sensors collect real-time data, and analytics tools detect patterns and anomalies that indicate a progressive deterioration of assets.
With this information, companies can schedule maintenance “just in time”, maximizing the useful life of equipment without risking unexpected failures and downtime.
Predictive maintenance maturity index
Predictive maintenance is a rapidly growing strategy with more and more adopters in the industrial world.
However, it has evolved significantly over the years, reaching various levels of maturity, culminating in what we now know as Predictive Maintenance 4.0.
- Level 1 – Visual inspections: technicians evaluate equipment conditions through physical inspections based on experience and direct observation.
- Level 2 – Instrument inspections: in addition to visual observation, this level uses specific measuring instruments to gather data on certain parameters, complementing technical expertise.
- Level 3 – Real-time condition monitoring: sensors continuously monitor equipment conditions, such as temperature or vibration, generating alerts based on predefined limits.
- Level 4 – Predictive Maintenance 4.0: this level represents the highest degree of maturity, leveraging advanced technologies like big data, machine learning, and artificial intelligence. On the one hand, it enables not only the prediction of failure timing but also recommends preventive actions based on historical data analysis, current conditions, and performance patterns. On the other, it is highly useful in identifying complex patterns and anomalies that traditional methods might overlook, allowing for timely interventions and optimizing asset efficiency.
Benefits of predictive maintenance
The progressive adoption of predictive maintenance techniques, at any of the four levels, brings significant benefits for industrial companies. Some of the most notable advantages include:
- Reduction in unplanned downtime: failure prediction enables maintenance planning at less critical times for production.
- Optimized maintenance costs: costly emergency repairs are avoided, and unnecessary interventions are reduced, optimizing the maintenance budget.
- Extended asset life: timely intervention prevents premature wear, extending the useful life of equipment and delaying replacement needs.
- Efficient spare parts management: accurate failure prediction helps improve spare parts inventory management, optimizing availability without excessive storage costs.
- Improved workplace safety: early problem detection contributes to maintaining a safe environment, reducing risks associated with unexpected failures.
CMMS for advancing your predictive maintenance strategy
Using a CMMS is essential for industrial companies seeking to proactively manage maintenance in their production plants and lay the foundation for a predictive strategy.
1. Centralization of maintenance data
A CMMS like MapexMM allows centralized storage of all asset information and maintenance history, offering a complete view of each equipment’s status.
This centralization facilitates the analysis of wear and failure patterns, enabling data-driven decision-making and improving predictive capabilities.
2. Integration with sensors and parameter monitoring
The true value of a CMMS in predictive maintenance lies in its ability to connect with IoT sensors that collect critical real-time data, such as temperature, vibration, or pressure.
These data enable continuous equipment condition monitoring, and combined with advanced algorithms and AI tools, can generate recommendations for corrective or preventive interventions.
3. Automated alerts and recommendations
The CMMS processes and analyzes captured data, generating immediate alerts when deviations from normal parameters are detected.
These alerts help anticipate the optimal timing for maintenance, based on a correlation of current and historical data, thereby minimizing unexpected failures and extending asset life.
4. Data visualization and predictive analysis
The CMMS platform offers tools to visualize data in intuitive charts and reports, making it easier to identify trends and forecast potential failures.
These reports are essential for maintenance managers, as they provide a complete and accurate view of equipment conditions, improving intervention planning.
5. Resource optimization and inventory management
A CMMS allows for resource optimization and precise spare parts inventory management.
By anticipating which components are at higher risk of failure, companies can schedule necessary purchases, avoiding both stock shortages and excess inventory.
6. Improved work order management
The system helps efficiently manage work orders by assigning interventions to the appropriate personnel and optimizing resource availability.
This ability to precisely coordinate scheduled interventions reduces downtime and enhances the efficiency of the maintenance team.
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