DiagFit and its Predictive Maintenance: A Strong Support for Industry and R&D
DiagFit, a predictive maintenance software, contributes to strengthening industrial equipment and shaping a more reliable, secure, and sustainable industry.
DiagFit enhances equipment robustness and supports R&D
With DiagFit, Amiral Technologies establishes itself in the field of predictive maintenance in a sustainable manner. Through a machine learning system specialized in managing industrial time series, DiagFit is capable of adapting to any type of equipment, from new to older, without relying on a history of failures. This is what is known as blind mode fault detection. The tool predicts failures in industrial equipment by utilizing an algorithm that analyzes problematic data that can lead to a breakdown. The goal is to ensure the operational continuity of production assets by leveraging the data collected by the software. The ultimate aim is to provide viable and sustainable solutions to industrial companies while enabling cost savings and minimizing their environmental impact.
Predictive maintenance extends equipment lifespan
Amiral Technologies has chosen machine learning to develop DiagFit. It is the most suitable solution for successfully predicting failures in blind mode. The tool does not need to know the specific equipment it analyzes, nor does the domain expert need to possess programming skills.
The software has a predictive purpose, meaning that it sends the initial abnormal signals to the domain experts who interpret the information themselves and determine the need for intervention on the machines. The real-time transmission of analysis results facilitates decision-making for the experts.
Replacing functional equipment with defects would increase costs for the company unnecessarily. For industrial companies aiming to be at the forefront of their industry, extending the lifespan of assets is essential and can be achieved through predictive maintenance.
Certain malfunctions can be quickly addressed without resorting to equipment replacement. They can be either software anomalies or physical defects, such as a cut in a pipe that can be easily repaired. In such cases, a cost-effective restoration is possible, leading to an extended lifespan for the problematic asset.
DiagFit enables optimal equipment utilization
One of the many advantages of DiagFit is the precision of the interpreted data. The software can indicate the wear rate of a component, allowing experts to determine its level of weakness. Based on this data, the experts conduct an analysis that explains the failure and the need for component replacement.
Before replacing the component, considerations can be made regarding the equipment’s usage modalities. For example, if a sensor indicates abnormal humidity levels that could affect the equipment’s performance, the experts can guide a simple and time-saving solution: relocating the equipment to a dry area.
By applying predictive maintenance methods, unnecessary and costly measures can be avoided. The experts connected to DiagFit can rely on the software’s analyses to ensure optimal utilization of the assets.
How does the data provided by DiagFit benefit manufacturers?
Manufacturers expect feedback from their customers, which is often not shared due to the lack of time for industrial companies. Quality control often takes a back seat, despite its importance.
By implementing predictive maintenance in companies, the goal is to quickly identify assets with flaws and resolve the resulting problems.
These advantages enable industrial companies to easily determine the appropriate course of action: seeking better components from different suppliers and/or providing feedback to manufacturers to contribute to their R&D efforts. As a result, products can be improved, and industrial sites will be better equipped and less likely to experience breakdowns.
More robust equipment for a responsible industry
Another positive consequence is the impact on the environment. By providing higher-quality models, manufacturers directly address the core issues.
As a result, there are fewer returns of defective equipment, extended lifespan of assets, reduced industrial waste, reduced material and energy resources allocated to maintenance, and more. Thus, the industry 4.0 adapts to changes in production methods without impeding the energy transition.
