Security, productivity, and guaranteed availability thanks to anomaly detection
At the forefront of Industry 4.0, anomaly detection addresses the increasingly demanding security and productivity challenges facing today’s businesses.
By using artificial intelligence to predict equipment failures and enable repairs before damage occurs, anomaly detection helps ensure the operational continuity of production facilities and reduce the risk of accidents on industrial networks.
What is anomaly detection?
With DiagFit, anomaly detection is a machine learning process that involves building a “normal” operating pattern for a piece of equipment based on healthy data in order to identify any deviations from this normality that could indicate a problem with the device.
This feature is used in particular in predictive maintenance, to inform technicians of a possible fault or sign of aging in a device before it breaks down. In DiagFit, anomaly detection algorithms have the advantage of being able to be implemented quickly, without the need for a history of failures, which is often complicated to implement due to the wide variety of data involved.
How does anomaly detection improve a company’s performance?
Dans l’industrie 4.0, les sites de production fonctionnent en flux tendu et les stocks de matières premières, comme les stocks de pièces de rechange, sont souvent réduits à zéro.
In Industry 4.0, production sites operate on a just-in-time basis and stocks of raw materials, such as spare parts, are often reduced to zero. While this method reduces costs and production times, it requires meticulous management of equipment maintenance, at the risk of compromising the operationality of the production line while the necessary parts are being delivered to restore equipment to working order.
However, traditional maintenance methods do not meet this requirement:
- Corrective maintenance, which involves working on equipment after a breakdown has been detected, often has to be carried out urgently to limit machine downtime. These operations require more human and material resources than would have been required for preventive maintenance.
- Preventive maintenance, which consists of checking the condition of equipment according to a pre-established schedule based on its theoretical wear and tear, often proves ineffective. On the one hand, some devices must be completely dismantled for analysis, which results in costly downtime and high engineering costs. On the other hand, there is usually an unpredictable gap between the actual wear and tear of a device and its theoretical wear and tear, and these preventive maintenance operations often prove unsuccessful.
Predictive maintenance, which relies on an anomaly detection system, is therefore becoming an essential component of these new production methods. Thanks to IoT sensors capable of retrieving data from industrial equipment and transmitting it to predictive maintenance software that detects any deviation from a device’s normal operating model, anomaly detection allows technicians to anticipate maintenance interventions to avoid unexpected machine downtime and ensure the availability of spare parts without overloading inventory. In many cases, anomaly detection also means that repairs are less extensive than they would have been if the equipment had actually broken down.
The use of an anomaly detection solution therefore ensures the operational continuity of the production line, reduces maintenance costs, and avoids losses caused by unexpected breakdowns.
