Solution overview

RSIMS is a predictive maintenance platform developed by Reliability Solutions (ReliaSol). This advanced system utilizes artificial intelligence (AI) and machine learning (ML) to predict and prevent equipment failures, optimize maintenance schedules, and enhance operational efficiency. The main objectives of RSIMS are to minimize unplanned downtime, reduce maintenance costs, and extend the lifespan of industrial assets. By providing actionable insights and maintenance recommendations, RSIMS helps companies ensure the reliability and efficiency of their machinery and processes.

Key features

AI-Driven Predictive Models: Utilizes sophisticated AI algorithms to predict equipment failures before they occur. Anomaly Detection: Features a dedicated anomaly detection module that identifies irregularities in machine operations, even with limited historical data. User-Friendly Interface: Designed for ease of use, allowing users with basic data analysis knowledge to perform full-value analytics. Prescriptive Maintenance: Offers recommendations for optimal maintenance actions based on the predictive analysis. Comprehensive Data Integration: Integrates data from various sources, including IIoT devices, to provide a holistic view of asset health.

Integration and adaptability in manufacturing environments

RSIMS is designed to seamlessly integrate into existing manufacturing processes and systems. It is compatible with common manufacturing technologies and equipment, making it adaptable to a wide range of industrial environments. The system can be customized to meet specific manufacturing requirements, ensuring that it aligns with the unique needs of each organization.

Implementation support and training for manufacturers

ReliaSol provides extensive support services to manufacturing companies during both the implementation and post-implementation phases. These services include: Training Programs: Comprehensive training for staff to maximize the benefits of RSIMS. Ongoing Support: Continuous technical support and maintenance to ensure smooth operation. Consulting Services: Expert consulting to tailor the solution to specific business needs and optimize its implementation.

Sustainability, compliance and certifications in the EU

RSIMS complies with relevant environmental regulations and industry standards in the EU. The platform supports sustainable practices by optimizing maintenance schedules and reducing unnecessary resource consumption. Additionally, ReliaSol ensures that RSIMS adheres to industry best practices for performance and sustainability, validating the solution`s effectiveness through certifications and accreditations.

Example of successful implementation of solution in manufacturing company.

Case study: Early detection of robot drive failures (Volkswagen Motor Polska) OBJECTIVES: - Implementation of Predictive Maintenance methodology for early detection of robot drive failures - Reduce downtime and repair costs RESULTS: - Monitoring and early detection of changes in signals indicating progressive wear of robot drives - Identification of faults that adversely affect the working conditions of the robot SUBJECT OF THE PROJECT & CHALLENGE The subject of the project involves automatic monitoring of ten robots on three production lines. The monitoring focuses on early detection of failure symptoms by tracking anomalies in signals characterizing the robots' operation. The challenge is to implement Predictive Maintenance methodologies that enable early detection of robot drive failures and reduce the cost of equipment downtime and repairs. SOLUTION The solution used is an early failure detection system based on anomaly tracking algorithms implemented in RSIMS Apps. The results so far in the project have been achieved based only on robot sensor data provided by the robot controller (including torques, temperatures) and the machine PLC. The application runs online and the data from the client's infrastructure transmitted via https protocol using due security standards. -> CASE 1 / Sensor data analysis Overruns characteristic of the degradation of the axis 5 drive were identified in the application. The propagation of the damage was observed against the parameters of the other drives, which did not deviate from the norm, and the replacement of the robot wrist itself was carried out as planned. Summary The solution, based on RSIMS Modules, allowed early identification of the type of failure and replacement of only faulty components on a scheduled basis, reducing both downtime and repair costs. Without monitoring of the robot's parameters in RSIMS Apps, there would have been further propagation of failures and emergency stoppage of the robot. The costs in such a case are definitely higher, and this is related to the potentially larger scope of the overhaul and the different cost of unscheduled downtime and execution of repair work in this mode. The estimated savings in this one case are in the range of EUR 25-35 thousand. BENEFITS: Benefits to date include reduced repair and downtime costs in the event of drive failures - among others, such as those shown in the example above. An additional benefit is that, using the application, it is possible to detect and fix faults in the robot's hardware early on, which, due to the nature of the lines being monitored, have so far only been identified when they caused them to stop. An example would be faults and leaks in the pneumatics system that cause deterioration of the robot's loading conditions - prolonged operation with this type of fault contributes to earlier degradation of the drives of the most heavily loaded robot axes.

Case studies or testimonials showcasing the impact of the solution on real-world operations.

Link to the solution video or pdf technical documentation.

Link: https://youtu.be/tHXgO9jL9L8

Expected return on investment (ROI)

Cost overview: implementation and ongoing expenses