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The attention of production directors and maintenance specialists has for several years been directed towards a predictive maintenance strategy as the potential optimal solution and alternative to the maintenance methods used so far. Today, the decision-makers that are responsible for process optimization in enterprises are no longer asking whether it is worthwhile to implement predictive maintenance (PdM), but what conditions must be met for the strategy to bring maximum results.
Predictive and prescriptive maintenance solutions are widely used by companies where the correct operation of machines is crucial for their operations. At a time of Industry 4.0 and increasing automation of the industry, it seems obvious that the above statement applies to the majority of production companies operating on the market. Therefore we see that a PdM strategy is already applied in a wide range of industries, on a variety of processes, installations and machines. ReliaSol PdM solutions are successfully implemented on industrial furnaces, turbines, reactors, tanks, compressors, chillers, evaporators and many others.
Solutions implemented by ReliaSol use both algorithms based on Machine Learning (ML) and ‘classical’ diagnostic algorithms, based on statistical and vibration analysis (temporal and spectral). Appropriately selected algorithms allow to include both small-scale installations (e.g. installations in large chemical plants, blast furnaces) and basic machines for each process (e.g. rotating machines). Thanks to a wide range of applied technologies and cooperation with partners, we are able to create a model for almost any device, taking into account its technology and business needs.
The RSIMS platform (ReliaSol Intelligent Maintenance System) created by ReliaSol is a digital platform that utilizes AI to predict failures, optimise processes, to improve quality and safety, and reduce energy consumption. Apart from its accuracy of prediction (up to 96%), RSIMS is distinguished by a short Time-to-Value period, higher scalability and a proven track record of successful implementations.
The RSIMS platform gives insights into the operations and work of machines, ensuring a high level of prediction efficiency and facilitating optimal business decisions.
RSIMS has been designed in such a way that a user, with only basic knowledge in the field of data analysis, would not only be able to perform full-value analytics, but also maintain the predictive power of the built models in the event of changes in the machine’s operating process. The RSIMS system also has a dedicated anomaly detection module. This enables the implementation of predictive maintenance strategies for facilities that do not have sufficient historical data or never recorded a failure.
Our solution is a perfect fit for companies on their digital journey towards optimizing their processes, improving quality, and maximizing production profits. RSIMS supports making the optimal decision and gives recommendations for operations and maintenance based on the provided data.
In the world where we cannot control so many things we offer you our best practices and proven solutions.
What can you control with AI-driven tools for Predictive Maintenance? How can you reduce energy consumption using them?
Let’s meet at the 13th edition of SYMAS in Krakow! The fair covers a wide spectrum of topics related to the optimization of production processes.