Prognostics & Health Management (PHM)
Highlights
Multi-source sensing and data-driven prognostics for early warning and RUL prediction
Targeting motors, pumps, fans, slopes, dams, gates and long-distance energy pipelines, PHM combines online sensing of vibration, temperature, displacement, seepage and strain with physics- and data-driven models for early warning, condition assessment and remaining-useful-life prediction.
Highly varied monitored assets: electromechanical objects such as motors, pumps and fans coexist with geotechnical ones such as slopes, dams, gates and long pipelines, with entirely different failure mechanisms and time scales.
Multi-physics coupling of fault symptoms: vibration, temperature, displacement, seepage and strain must be correlated synchronously — a single quantity cannot pinpoint the true cause.
Weak early features easily buried in noise: early-degradation signals are low in energy and overlap with normal conditions, requiring long-term monitoring and trend analysis.
Continuously changing conditions: speed, load, water level and season cause fault features to drift, so condition recognition and normalisation are needed before comparison.
Scarce fault samples: failures of core equipment are rare and costly, so large-sample machine learning is not viable — physical mechanisms and statistical models must be combined.
Output feeds O&M decisions: monitoring conclusions support shutdown maintenance, spare-part procurement and safety rulings, demanding high warning accuracy and interpretability.