Prognostics and Health Management (PHM) is a key technology to evaluate the current health condition (health monitoring) and predict the future degradation behavior (health prognostics) of an engineered system throughout its lifecycle.
Sensing FunctionTo ensure high damage detectability by designing an optimal Sensor Network
Reasoning Function
To extract system health relevant information in real-time and to classify system health condition
Prognostics Function
To predict remaining useful lives (RULs) of engineered systems in real-time
Health Management
To enable optimal decision making on maintenance of engineered systems
Health Sensing Function
Objective
To Sense the signal of various system in a cost-effective and high detectable way
Sensor Selection
SensorType
Thermal Sensor
AcousticEmissionSensor
Acc.Sensor
Strain Sensor
Current VoltageSensor O
ilSensor
LoadSensor
Flow Sensor
Appli-cations
Electronics,
Bearings Bearings,
Gearboxes,Engines Bearings,
Gearboxes,
Engines Blade Bearings Engine,
Switches,
Cables
Bearings,
Gearboxes,
Engines
Rotor,
Dams,
Bridges
Hydraulic Component
Sensor Network (SN) Design
To optimize sensor position
To optimize sensor direction
To minimize No. of sensors
While maximizing detectability1
1Ability to detect the health state
Ex) Power transformer
Before SN design
After SN design
Detectability
No. of sensors
Wang, Pingfeng, et al. "A probabilistic detectability-based sensor network design method for system health monitoring and prognostics." Journal of Intelligent Material Systems and Structures (2014): 1045389X14541496.
Health Reasoning Function
Objective
To extract system health relevant information in real-time and to classify system health state
Signal Processing
To enhance detectability of health relevant signals by minimizing obstacles (e.g. noise)
Noisy signal Health relevant signal
Statistical data modeling
To define quantified values representing health state of the system
Health data (HD)
(Multi-variables)
Statistical moments(e.g. RMS)
Model parameters
Health Index (HI)
(Unified variable)
CAE for Health reasoning
To simulate system failure condition by CAE
Hu, Chao, et al. "Copula-based statistical health grade system against mechanical faults of power transformers." Power Delivery, IEEE Transactions on 27.4 (2012): 1809-1819.
Health Prognostics Function
Objective
To define Health Index (HI) and to predict remaining useful lives (RULs) of engineered systems in real-time
Model-based Prognostics
Assumption: Known PoF; few run-to-failure data
Key element: Online identification of PoF-based degradation model
Data-driven Prognostics
Assumption: Massive run-to-failure data
Key element: Offline training and online prediction processes
Loading Signals
Simulation
Identify Model
Simulate with Loading
Response Signals
Estimation
Update Parameters
Update & Project HI
Predicted RUL
TestingSignals
Online Process
Extract Online HI
Project or Interpolate
Training Signals
Offline Process
Extract Offline HI
Build Health Knowledge
Hu, Chao, et al. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life." Reliability Engineering & System Safety103 (2012): 120-135.
Application: Smart Factory
SMART FACTORY
Equipment
Health
Product Health
Equipment Health Management
Health Prognosis of Product & Equipment
Integrated Health Management System for Product & Equipment
Big Data
Deep Learning
Domain Knowledge
Integrated Health Prediction/Management System for Product & Manufacturing Equipment