Python project, based on Condition monitoring of hydraulic systems Data Set -> (link)
Usage of pandas and sklearn ML technics in order to predict a hydraulic pump failure based on sensor readings.
Data Set Information:
The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.
Attribute Information:
The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.
Attribute Information: The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are:
Sensor Physical quantity Unit Sampling rate PS1 Pressure bar 100 Hz PS2 Pressure bar 100 Hz PS3 Pressure bar 100 Hz PS4 Pressure bar 100 Hz PS5 Pressure bar 100 Hz PS6 Pressure bar 100 Hz EPS1 Motor power W 100 Hz FS1 Volume flow l/min 10 Hz FS2 Volume flow l/min 10 Hz TS1 Temperature °C 1 Hz TS2 Temperature °C 1 Hz TS3 Temperature °C 1 Hz TS4 Temperature °C 1 Hz VS1 Vibration mm/s 1 Hz CE Cooling efficiency (virtual) % 1 Hz CP Cooling power (virtual) kW 1 Hz SE Efficiency factor % 1 Hz
The target condition values are cycle-wise annotated in ‘profile.txt‘ (tab-delimited). As before, the row number represents the cycle number. The columns are
1: Cooler condition / %:
3: close to total failure.
20: reduced effifiency.
100: full efficiency.
2: Valve condition / %:
100: optimal switching behavior.
90: small lag.
80: severe lag.
73: close to total failure.
3: Internal pump leakage:
0: no leakage.
1: weak leakage.
2: severe leakage.
4: Hydraulic accumulator / bar:
130: optimal pressure.
115: slightly reduced pressure.
100: severely reduced pressure.
90: close to total failure.
5: stable flag:
0: conditions were stable.
1: static conditions might not have been reached yet.
Data set Citation Request: Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.