Maintenance management is an aspect that affects many sectors: from the production industry, mining, but also logistics, transport, fashion and luxury, hospitality, etc., all organizations need to be able to rely on reliable and performing machinery and infrastructure.
The object of maintenance, then, is not only the production machinery, but all those physical assets that allow a company to manage production and business optimally (computers, equipment, industrial machinery, etc.) and which must be efficient and in good working order.
It should be added that each sector has its own peculiarities: if for a logistics company a fundamental asset is given by the transport fleet, for a manufacturing company production plants are the fundamental component.
Malfunctions, machine downtime failures represent, in fact, a loss not only in terms of cost to be incurred, but also in terms of production failure, unexpected interruption of operation, failure to comply with orders and so on. For this reason, companies are more and more careful to contain and foresee the damage then act retrospectively:
Predicting failure helps prevent it and prolong the machinery lifetime, with a positive impact on cost incidence and resource optimization.
What is meant by Predictive Maintenance?
Predictive maintenance is an advanced form of preventive maintenance that allows to identify the remaining time before the occurrence of the fault and then to prevent it, establishing the most convenient time to program the maintenance actions on the machinery.
This type of maintenance is enabled by IoT technologies that allow the continuous real-time monitoring of machines and the collection of a large amount of data. All this information is transformed through mathematical models into precise indicators that allow to evaluate the state of actual deterioration of the machinery and, therefore, to establish the most advantageous moment to program the maintenance actions.
For all these reasons, predictive maintenance has become one of the pillars of Industry 4.0.
We see in detail the differences between reactive, preventive and predictive maintenance, in terms of benefits, costs and opportunities.
How does Predictive Maintenance (PdM) work?
Predictive Maintenance is based on technologies and tools that allow real-time condition monitoring using a data-driven approach.
The data on machinery, faults and past events and the data acquired in real-time by monitoring the machines through sensors and IoT components are acquired and made to be delivered in an AI (Artificial Intelligence) platform, where they are processed, analyzed and used to instruct a predictive model: an algorithm able to predict the occurrence of certain events based on specific indicators. Such events may be the possibility or not of the occurrence of a failure and the determination of a risk level.
The result is a new maintenance model, based on real-time monitoring, capable of generating countless benefits.
Benefits of predictive maintenance
Compared to Preventive Maintenance, there is a paradigm shift: from the intervention implemented to solve a problem, to an efficient planning of maintenance activities aimed at reducing costs and extending the life of the plants themselves.
The main advantages of preventive maintenance can be summarized in:
- Prevent the occurrence of failure and unexpected downtimes (identifying anomalies allows you to act before the occurrence of the malfunction avoiding the failure itself);
- Reduction of costs (maintenance, non-production, inactivity, purchase of new assets, missed orders, etc.);
- Increased productivity and safety at work;
- High quality and production performance thanks to optimal asset conditions.
How to structure a predictive maintenance system?
There are conditions necessary to implement predictive maintenance:
- Data collection system through IoT and sensor technologies;
- Predictive Maintenance Software:Software CMMS (Computerized Maintenance Management System), that is a program that allows to manage the assets and to plan the maintenance through dashboard;
- AI Machine Learning tool platforms for the implementation of predictive models and data analysis algorithms.
When is it convenient to adopt Predictive Maintenance?
The convenience of a predictive maintenance system in the long term is so obvious that it has already been identified as a highway for many industries (manufacturing, mining, petrochemical, pharmaceutical, logistics and transport, etc.).
The investments needed to implement a predictive approach discourage especially small companies that have not yet enabled data-driven business models and that risk to accumulate a gap more and more compared to more structured companies, able to invest and therefore to contain costs, increase revenues and obtain higher profits already in the short term.
The Business Intelligence & Predictive Analytics result today the indispensable tools for those companies operating in hyper-competitive markets, which are not by chance the first to invest in this regard.
Here is a case study of Predictive Maintenance in the Fashion & Luxury sector.
Examples and Use Cases of Predictive Maintenance
- Manufacturing Industry: integrated sensors in machinery monitor vibrations, temperature, and other parameters to detect anomalies. This allows for predicting mechanical wear and scheduling maintenance before failures occur, reducing downtime.
- Wind Energy Plants: in wind farms, sensors monitor turbines to collect data on vibrations, temperature, and wind speed. By analyzing this data, it is possible to predict bearing and blade deterioration, planning maintenance to avoid unplanned shutdowns.
- Railways: trains and railway infrastructure, such as tracks and signaling systems, are equipped with sensors to monitor rail wear and brake conditions. Data analysis helps predict when a critical component may fail, ensuring service safety and reliability.
- Aerospace Industry: in aircraft engines, predictive maintenance is used to monitor parameters like engine vibrations, oil pressure, and temperature. This prevents in-flight failures, reduces maintenance costs, and enhances safety.
- Automotive: modern vehicles feature advanced sensors that monitor the engine, brakes, and other critical components. By analyzing the collected data, the system can predict maintenance needs, such as brake pad replacement or engine servicing, before failures occur.
- Petrochemical Sector: oil refineries use predictive maintenance to monitor pumps, compressors, and other critical equipment. Data analysis on pressure levels, vibrations, and temperature allows for predicting failures and scheduling maintenance without disrupting production.
- Data Centers: predictive maintenance is used to monitor servers, cooling systems, and power sources. Data on heat levels, humidity, and energy consumption are analyzed to prevent failures that could cause service interruptions.
- Healthcare Sector: hospitals apply predictive maintenance to critical equipment such as MRI machines and ventilators. By monitoring usage and performance data, failures can be predicted, ensuring equipment availability for patients.
Here, you can find a case study on the implementation of a predictive maintenance model for a Blue BI client in the Fashion & Luxury sector.
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