Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
SmartLogFounded in 2013, Smartlog is now known as a scale-up in the field of Industry 4.0 applications. We define success as ‘achieving exceptional results for our clients through digitalization and servitization that have a lasting impact on industrial businesses worldwide’. This principle has remained the cornerstone to underpin everything we do. Our clients choose us because we challenge convention to find the right solution that really works in practice, not just on paper.
Equipment & Machinery
Thanks to an asset performance health index on our dashboard, chiller manufacturers are constantly provided insight in the health of their assets. Failures can be predicted, manufacturers are directly alerted in case of problems so they can be resolved remotely and turnaround time for problem resolution will be reduced dramatically as a result of making industrials chiller internet connected.
- CONNECTIVITY PROTOCOLS
LPWAN LoRaWAN MQTT
A failing device not only results in downtime, but as well in faulty chiller components. Chiller connectivity however, guarantees process stability, process reliability and energy efficiency, since all parameters (pressure, temperature, current values…) are monitored and visualized on our dashboard. This way, we can make a prediction regarding the amount of days within the failure is likely to occur.
- DATA COLLECTED
Asset Performance, Downtime, Fridge Temperature, Pressure, Production Efficiency
- SOLUTION TYPE
- SOLUTION MATURITY
Mature (technology has been on the market for > 5 years)
- OPERATIONAL IMPACT
Impact #1 [Efficiency Improvement - Asset Utilization]
Asset performance health index.
Impact #2 [Efficiency Improvement - Production Uptime]
Failures can be predicted.
Impact #3 [Efficiency Improvement - Issue Response]
Turnaround time for problem resolution will be reduced dramatically.
- QUANTITATIVE BENEFIT
- USE CASES
Predictive MaintenancePredictive maintenance is a technique that uses condition-monitoring sensors and machine learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on predictive maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.