Figure 1.
Organization structure of the system based on internet of things technology
Figure 2.
Hardware components theory block diagram of the fuel pump sensor instrument node
Figure 3.
Hardware components theory block diagram of the IOT aggregation instrument node
Figure 4.
MCU control program flow chart of the IOT aggregation instrument node
Figure 5.
Module structure diagram of the IOT center computer monitor system software
Figure 6.
The structure of the expert system for oil well pump LSTM machine learning fault diagnosis
Figure 7.
Neural network module structure diagram
Figure 8.
LSTM machine learning model structure principle
TABLE I.
Top 20 records in the data set used by oil well pump machine learning
| Code | Date | Time | Oil Pressure (Mpa) |
|---|---|---|---|
| 1 | 6/1 | 0:00 | 1.69 |
| 2 | 6/1 | 1:00 | 1.81 |
| 3 | 6/1 | 2.00 | 2 |
| 4 | 6/1 | 300 | 1.97 |
| 5 | 6/1 | 4:00 | 1.84 |
| 6 | 6/1 | 5:00 | 2.01 |
| 7 | 6/1 | 6:00 | 1.62 |
| 8 | 6/1 | 7:00 | 1.89 |
| 9 | 6/1 | 8:00 | 2.14 |
| 10 | 6/1 | 9.00 | 2 |
| 11 | 6/1 | 10:00 | 2.06 |
| 12 | 6/1 | 1100 | 1.8 |
| 13 | 6/1 | 12:00 | 1.65 |
| 14 | 6/1 | 13:00 | 1.67 |
| 15 | 6/1 | 14:00 | 2.11 |
| 16 | 6/1 | 15.00 | 1.88 |
| 17 | 6/1 | 16:00 | 2.12 |
| 18 | 6/1 | 17:00 | 1.54 |
| 19 | 6/1 | 18:00 | 2.08 |
| 20 | 6/1 | 19:00 | 2.02 |
Figure 9.
Fault diagnosis and prediction scatter diagram of oil well pump oil delivery pressure training set
Figure 10.
Oil well pump oil delivery pressure test set fault diagnosis and prediction scatter diagram
Figure 11.
Comparison chart of machine learning actual value and predicted value of oil delivery pressure of oil well delivery pump