Remaining Useful Life (RUL) Application

For Machinery

This AI-Driven RUL application predicts Health State and Initial degradation time for bearing machinery using the XJTU-SY bearing dataset, a key resource in predictive maintenance research.

Configuration

Operating Environment

Select Operating Conditions:

Accelerometer Settings

Select Axis:

Signal preprocessing

Filtering Method

Select Filter:

Signal Extraction Methods

Time Domain Technique:

Frequency Domain Technique:

Prediction method

Prediction technique

Select technique:

Remaining Useful Life (RUL) Application

For Batteries

Battery

Sensors

Temperature

Voltage

Current

data

Aging cycles

Present cycle

Expected Remaining Cycles (Labels)

Beginning of Life

End of Life

Charge-Discharge Cycle Life

Start Animation

Stop Animation

Remaining Useful Life (RUL) Application

Prediction

Current, voltage, temperature, and time signals from the present charge-discharge cycle are fed into an AI model to predict the remaining charge-discharge cycles.

Remaining Useful Life (RUL) Application

For Machinery

Configuration

Operating Environment

Accelerometer Settings

Signal preprocessing

Filtering Method

Signal Extraction Methods

Prediction method

Prediction technique

Preprocessed Signals

Health State PREDICTION

Remaining Useful Life (RUL) Application

For Batteries

Battery

Sensors

data

Prediction

AI model