Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor
Abstract
:1. Introduction
- A chiller operates continuously in response to outdoor and indoor environments, and it is difficult to distinguish between sensor faults based on the acquired data. Furthermore, situations in which a chiller operates under sensor faults are uncommon, and it is difficult to obtain fault data. Accordingly, the imbalance of the acquired data adversely affects the performance of the fault-detection models.
- There has been a lack of research on detecting faults in SCWT sensors and evaluating indoor thermal comfort and energy consumption by correcting sensor faults.
2. Methodology
3. Development of Virtual Sensor
3.1. LSTM-Autoencoder
3.1.1. LSTM
3.1.2. Autoencoder
3.2. Virtual Sensor for Fault Detecting in Physical Sensor
3.2.1. Virtual Sensor Based on LSTM-Autoencoder
3.2.2. Virtual Sensor Modeling
4. Case Study
4.1. Simulation Modeling
4.1.1. Target Building and System
4.1.2. SCWT Sensor Fault
4.2. Simulation Cases
4.3. Virtual Sensor Fault-Detection Performance Metrics
4.3.1. Accuracy
4.3.2. Precision
4.3.3. Recall
4.3.4. F-1 Score
5. Results
5.1. Virtual Sensor Fault-Detection Performance
5.2. Virtual Sensor Fault-Correction Effect
5.2.1. Indoor Set-Point Temperature Unmet Hours
5.2.2. Energy Consumption
6. Conclusions
- The evaluation of the fault-detection performance of the virtual sensor showed excellent results for various fault types. The virtual sensor detected all faults for the transient fault types (Cases A-1 and A-2), with an F-1 score of 1.000. The virtual sensor detected the majority of faults for the continuously fixed fault types (Cases B-1 to B-4), with F-1 scores ranging from 0.9965 to 0.9997. However, the virtual sensor occasionally failed to detect faults with subtle offsets for continuous random offset faults (Case C) and faults with offsets that gradually increased or decreased over time (Cases D-1 and D-2), resulting in F-1 scores ranging from 0.9350 to 0.9846.
- The evaluation of indoor thermal comfort following fault resolution revealed that the impact of sensor faults on indoor thermal comfort in types with transient and random offsets varied depending on the occurrence frequency and offset of the sensor faults. In the case of faults with continuously fixed offsets and offsets worsening over time, the indoor set-point temperature unmet hours increased by up to 425% for negative fault offsets. Consequently, indoor thermal comfort was adversely affected.
- The evaluation of energy consumption following fault resolution revealed that the impact of sensor faults on energy consumption in types with transient faults and random offsets varied depending on the occurrence frequency and offset of the sensor faults. In the case of faults with continuously fixed offsets and those with offsets worsening over time, the chiller energy consumption decreased for faults with negative offset values, whereas the circulation pump and fan energy consumption increased. Conversely, for faults with positive offset values, the chiller energy consumption increased, whereas the circulation pump energy consumption decreased.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Ref. | Year | Method | Sensor Fault Type | Quantitative Result |
---|---|---|---|---|
[9] | 2004 | PCA | Fixed bias | Detection ratio: almost 100% |
Drifting | ||||
Precision degradation | ||||
Complete failure | ||||
[10] | 2010 | PCA | Fixed bias | Detection ratio: 96.4–100% |
[11] | 2022 | Boltzmann-machine | Fixed bias | F-1 score: 0.92–1.00 |
[12] | 2004 | GRNN | Fixed bias | Fault classification: O |
[13] | 2014 | Combined ANN | Fixed bias | False alarm ratio: 0.6–7.5% Missing alarm ratio: 0–8.3% |
Parameter | Value | ||
---|---|---|---|
Hyper parameters | Layers | LSTM encoder | 140 |
70 | |||
LSTM decoder | 70 | ||
140 | |||
Optimizer | Adam | ||
Activation function | ReLU | ||
Loss function | Mean squared error | ||
Batch size | 32 | ||
Epochs | 100 | ||
Input parameters | Outdoor air temperature | ||
Chiller energy consumption | |||
SCWT |
Input Parameter | Value | ||
---|---|---|---|
Building | Zone | Location | Daejeon, South Korea |
Type | Office | ||
Zone volume | 4800 m3 | ||
Indoor set-point temp. | 26 °C | ||
U-values | Exterior wall | 0.24 W/m2‧K | |
Window | 1.50 W/m2‧K | ||
Roof | 0.15 W/m2‧K | ||
Floor | 0.29 W/m2‧K | ||
Zone conditions | People | 117.24 W/person | |
Light | 6.89 W/m2 | ||
Equipment | 8.07 W/m2 | ||
System | Capacity | Supply fan | 2.7 m3/s |
Circulation pump | 0.003 m3/s | ||
Chiller | 70,000 W | ||
Simulation setting | Run period | 8/1 to 8/31 | |
Time step | 1 min |
Fault Type | Offset Type | Simulation Case | Offset | Fault Occurrence Scenario |
---|---|---|---|---|
Point anomaly | Point | A-1 | −3 °C | One hour each time, once a week |
A-2 | 3 °C | |||
Contextual & collective anomaly | Fixed | B-1 | −3 °C | From 16 August to 31 August |
B-2 | −1 °C | |||
B-3 | 1 °C | |||
B-4 | 3 °C | |||
Random | C | −3 °C to 3 °C | ||
Drift | D-1 | −0.1 °C/h (max: −10 °C) | ||
D-2 | 0.1 °C/h (max: 10 °C) |
Metrics | Simulation Case | ||||||||
---|---|---|---|---|---|---|---|---|---|
A-1 | A-2 | B-1 | B-2 | B-3 | B-4 | C | D-1 | D-2 | |
Accuracy | 1.0000 | 1.0000 | 0.9995 | 0.9998 | 0.9978 | 0.9980 | 0.9902 | 0.9510 | 0.8851 |
Precision | 1.0000 | 1.0000 | 0.9985 | 0.9994 | 1.0000 | 1.0000 | 0.9993 | 0.9479 | 0.8778 |
Recall | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9930 | 0.9937 | 0.9703 | 1.0000 | 1.0000 |
F-1 score | 1.0000 | 1.0000 | 0.9992 | 0.9997 | 0.9965 | 0.9968 | 0.9846 | 0.9733 | 0.9350 |
Offset | Negative Offset | Positive Offset |
---|---|---|
Point | Depended on offset value and frequency | |
Fixed | Increase | Negligible |
Random | Depended on offset value and frequency | |
Drift | Increase | Decrease |
Offset Types | Equipment | Negative Offset | Positive Offset |
---|---|---|---|
Point | Chiller | Depended on offset value and frequency | |
Circulation pump | |||
Fan | |||
Fixed | Chiller | Decrease | Increase |
Circulation pump | Increase | Decrease | |
Fan | Increase | - | |
Random | Chiller | Depended on offset value and frequency | |
Circulation pump | |||
Fan | |||
Drift | Chiller | Decrease | Increase |
Circulation pump | Increase | Decrease | |
Fan | Increase | - |
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Jin, S.; Jang, A.; Lee, D.; Kim, S.; Shin, M.; Do, S.L. Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor. Appl. Sci. 2024, 14, 1113. https://doi.org/10.3390/app14031113
Jin S, Jang A, Lee D, Kim S, Shin M, Do SL. Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor. Applied Sciences. 2024; 14(3):1113. https://doi.org/10.3390/app14031113
Chicago/Turabian StyleJin, San, Ahmin Jang, Donghoon Lee, Sungjin Kim, Minjae Shin, and Sung Lok Do. 2024. "Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor" Applied Sciences 14, no. 3: 1113. https://doi.org/10.3390/app14031113
APA StyleJin, S., Jang, A., Lee, D., Kim, S., Shin, M., & Do, S. L. (2024). Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor. Applied Sciences, 14(3), 1113. https://doi.org/10.3390/app14031113