Asset Monitoring Solutions

We use powerful Machine Learning algorithms to analyse circuit breaker first trip

PROFILE P3 features embedded algorithms to support rapid on-site analysis of circuit breaker defects. Historical data is stored in the cloud for each circuit breaker, allowing new test results to be compared with existing results. Asset managers are then able to access instantaneous results, trending and alerts.

• Immediate feedback: Cloud-based, onsite diagnosis
• Degradation analysis: Tracks circuit breaker trip status over time
• Fleet ranking: Comparison between different circuit breakers to schedule maintenance
• Continuous learning: ML Models constantly updated

INTEGO is the only monitoring solution for generators to meet IEC standards

IEC60034-27-2 describes the recommended test procedure for rotating machines, referencing consecutive load and temperature conditions in order to correlate Partial Discharge and operating conditions. Testing can be complex to carry out in the field. INTEGO enables testing to be carried out online, in real operating conditions. Featuring its own integrated web server, PD activity can be viewed under different operating conditions as an intuitive Partial Discharge Energy (PDE) map, identifying:

• The operating conditions of the machine
• In which operating condition Partial Discharge is smaller and in which is higher
• What is the predominant degradation mechanism

TOTUS is the only transformer monitoring solution that can detect arcing in a power transformer

Dissolved Gas Analysis (DGA) is typically used to detect discharge of high energy (D2), with Acetylene being the primary marker. The challenge for asset managers is little visibility on when the arc occurred and in which phase and winding, as well as no tracking on the number of arcing events.

Whereas other power transformer monitors can provide basic information identifying acetylene in the transformer, TOTUS delves deeper into arcing activities, identifying the phase and winding, as well as visibility on the number of arcing events. They key benefit is the ability to correlate the data and generate a deeper understanding of changes in the power transformer. The Image on the right demonstrates damage to the winding leads due to high energy discharges.

We use powerful Machine Learning algorithms to analyse circuit breaker first trip

PROFILE P3 features embedded algorithms to support rapid on-site analysis of circuit breaker defects. Historical data is stored in the cloud for each circuit breaker, allowing new test results to be compared with existing results. Asset managers are then able to access instantaneous results, trending and alerts.

• Immediate feedback: Cloud-based, onsite diagnosis
• Degradation analysis: Tracks circuit breaker trip status over time
• Fleet ranking: Comparison between different circuit breakers to schedule maintenance
• Continuous learning: ML Models constantly updated