Preventing stoppages in the operation of equipment units in order to enable the transition to condition-based repairs. The system predicts the equipment’s operation using mathematical models and determines the date and time of the stoppage.
Evaluating the technical condition of equipment in real time. The system visualizes abnormal operating modes, displays the equipment’s operating parameters, and puts out alerts about impending malfunctions and failures.
- Finding out the cause of any equipment breakdown. The system classifies the cause of the breakdown and displays it on the monitor screen. The system detects the slightest deviations from normal operation, analyzes them, and, after processing the received data, informs the user in advance about the possible failure of a particular unit;
- Digitizing the knowledge of the company’s engineers and experts. With the help of machine learning algorithms, the system is constantly trained by engineers and builds a more accurate prediction with each new analysis.
Equipment Operation Monitoring and Condition Prediction System
- Instrument-making industry
- Rail transport
- Metallurgy industry
- Oil and gas industry
- Power industry
- Mining industry
- Chemical industry
- Shipbuilding industry
- Manufacturing industry.
The Clover PMM solution enables the user to predict equipment failures a few weeks or months before their actual occurrence. As soon as you receive such an opportunity, you will start improving your corrective maintenance system on a condition-based principle. One of the main advantages of our solution is an open microservice architecture, which provides for its integration with the majority of corporate software applications known to us. The components of the Clover PMM system support a high degree of flexibility through the use of integrated development environments.
The solution was designed in conformity with the latest trends in technology, leveraging the accumulated experience. The seminal machine-to-machine (M2M) data exchange approaches and concepts used in IoT and IIoT have been captured in the developed mechanisms for collecting data from equipment and, most importantly, in what has become the distinctive feature of Clover PMM – its capability for deployment on existing industrial IoT/IIoT platforms.
Big Data processing techniques have been used for multi-temperature data warehousing, which allows Clover PMM to handle highly dynamic data streams, as well as amounts of accumulated data in excess of 10 PB.
Machine Learning and Deep Learning have made it possible to enhance the solution’s scientific component and to detect the causes of failures that are invisible when using analogs.
The system’s implementation will allow businesses from different sectors of the economy to gain certain advantages. Economy-wise, it will translate into a decrease in operating expenses, including the cost of ownership of technology-intensive equipment, which will go down by 10%. Business processes’ efficiency will increase by 50–70%. Adjusting the amounts of repair work performed according to standard repair routines, and cutting back on any repair work in excess of standard specifications will boost productivity by 13%. Less unplanned downtime will ensure that your production assets are utilized 10% more efficiently. Finally, overall energy efficiency will increase by at least 17% and maintenance costs will decrease by the same amount.