PROGNOST®-NT is the world’s leading diagnostic system that uses Neural Networks and Deep Learning technology for automated Failure Pattern Recognition of Rotating Equipment. More than 20.000 sensor samples per second are analyzed automatically in real-time. The system provides actionable clear text messages to operators, indicating the affected components, the failure mode and the percentage of failure severity.
PROGNOST®-Predictor uses the patented Confidence Factor and Two Factor alarm logic to detect failures in Roller Bearings and complex gearboxes. Combining all of the PROGNOST®-Predictor capabilities results in optimal health of your machine and supports you to get your Preventive Maintenance becomming Predictive Maintenance management.
Monitoring and online diagnostics of Rotating Equipment Rotating Equipment forms the majority of production assets. Pumps, compressors and gearboxes are irreplaceable in the oil, gas and petrochemical industry. Debottlenecking activities which [...]
“Intelligent Machine Monitoring” is a video production from experts for experts. Viewers learn basics of signal analyses as well as advanced topics to improve the results of their daily work. Enjoy the movies and feel free to share your thoughts in the comments section. We are also open for your suggestions for topics to be explained and discussed.
Tutorial for understanding the important basics of rolling element bearing monitoring. Learn what BPFO, BPFI, 2BS and FTF mean and how to detect impending bearing failures. The basis are time waveform readings from acceleration sensors that need to be transformed into FFT spectra with the specific frequency components.
After a longer failure history with this gearbox, the customer decided to replace the installed spot measurement by a reliable online monitoring system. The customers´ decision-making process was supported by an open communication between the operator, the PROGNOST Systems team and the gearbox OEM.
After two months of unscheduled downtime, the customer asked our Customer Support team for support during compressor startup. Depending on the system and the local circumstances, this type of support can be done on site or via remote access.
Residual abrasive contaminants in new process units can cause rapid wear of rider bands in reciprocating compressors. Those abnormal wear conditions can be detected at an early stage by continuously monitoring the proximity measures. As a result, costly damages to the compressors can be avoided.
Condition monitoring can substantially reduce the cost of consequence by avoiding catastrophic failures and reducing the frequency of incidents by providing realtime and continuous diagnoses of bad actors and all relevant components.