Predictive maintenance at a glance
With predictive maintenance, the proper operation of equipment can be monitored in real time. Based on continuous analysis of a number of precise indicators, this type of maintenance detects any wear, change or potential incident before it occurs. This approach to anticipating faults is a major step forward for maintenance. Thanks to data analysis, the organizations can intervene only when necessary, and thus optimize the time and cost of their operations like never before. More than any other form of maintenance, such as preventive maintenance, predictive maintenance is ideal for improving performance.
What distinguishes predictive maintenance from other types of maintenance?
Predictive maintenance and data analysis are gaining ground in companies. When data is properly exploited, anomalies are detected before a fault occurs, thus providing greater flexibility for the organization.
And when it comes to analysis, predictive maintenance goes further than any other form of maintenance. It is based not only on a machine’s condition and key indicators, but also on its history, state of wear and a wide range of other parameters, in order to anticipate potential faults. The data is collected directly from the equipment, and thoroughly analyzed for targeted maintenance.
So, unlike condition-based maintenance, which involves taking action only when certain precise indicators (temperature, vibration, pressure) vary abnormally, or preventive maintenance, which is more generic, predictive maintenance considers each piece of equipment individually. The repairs are more accurate and better targeted, without wasting time or interrupting production.
What are the advantages of predictive maintenance?
This reduction in downtime is one of the major benefits of any predictive maintenance strategy. Determining the sources of faults in advance means intervening on equipment while it’s still working, and keeping units operational at all times. But that’s not all! The benefits of Maintenance 4.0 extend to the entire organization: manpower and material resources are better allocated, and internal operations are improved.
Predictive maintenance means lower intervention costs, as technicians only intervene when really needed, and less frequent replacement of machines, as timely repairs limit wear and tear. The economic impact is significant, especially for large fleets and complex equipment.
Last but not least, a better-maintained fleet means greater reliability. Reliability of production, with more consistent quality, but also of working conditions. The safety of premises and agents, crucial for organizations, is thus better guaranteed.
What technologies power predictive maintenance?
At the root of predictive maintenance lies data analysis. This data is collected using several technologies to optimize maintenance:
- IoT: the connected equipment is a valuable source of operating data (pressure, temperature, etc.). Together with precise conditions, these can be used to identify anomalies and trigger alerts accordingly.
- Artificial Intelligence (AI): thanks to Machine Learning, it is possible not only to take into account the characteristics of each piece of equipment, but also to identify new types of dysfunction. AI also provides highly customized advice for their solution.
- Big Data and Smart Data: the sources of data are multiplying, and with them the formats and nature of this data. As a result, the analysis is richer and smarter, especially with AI, which instantly isolates and uses the most relevant data.
How do you implement an effective predictive maintenance strategy?
The implementation of predictive maintenance requires a number of preliminary steps. Particular attention should be paid to:
- Design a suitable program to monitor the right equipment and indicators
- Equip your machines with sensors and connect them up
- Integrate the systems so that data from the entire fleet is centralized
- Plan its maintenance and refine monitoring through experience.
In this context, Computerized Maintenance Management System (CMMS) software plays a key role. Indeed, CMMS provides an invaluable link between maintenance teams and the machine fleet. New CMMS software, known as CMMS 4.0, integrate data collected by IoT, analyze indicators and provide comprehensive, reliable reports. This makes it possible to implement predictive maintenance in the true sense of the word: all events, even the most unforeseen, are anticipated, and the maintenance organization is constantly improved.
Why is predictive maintenance the future of industry?
Intelligent use of data is an essential prerequisite for industrial success. With early detection of anomalies, the organization evolves in depth. Predictive maintenance not only saves time and creates more flexible teams, but also provides real decision-making support and reliable assistance in optimizing performance on an ongoing basis, thanks to maintenance 4.0 solutions.
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What kind of industries can benefit from predictive maintenance?
Any industry using machines can benefit from predictive maintenance. For example, the aerospace industry uses predictive maintenance to monitor the condition of aircraft engines, which can prevent in-flight failures and improve safety.
How can predictive maintenance contribute to reducing operating costs?
By anticipating the breakdowns, the predictive maintenance helps to avoid the unplanned downtime that can be costly in terms of lost production. For example, in a manufacturing plant, a machine breakdown can bring production to a standstill, costing thousands of euros per hour.