This document discusses using an autoencoder for anomaly detection in a geothermal power plant. It provides an overview of autoencoders, explaining that they are unsupervised neural networks used to compress input data into a latent space representation, then reconstruct the output. The document outlines the encoder and decoder parts of an autoencoder. It then discusses using the latent space of an autoencoder for dimensionality reduction, denoising, and anomaly detection. The rest of the document describes applying these techniques to a geothermal power plant dataset, including preprocessing steps like missing data imputation, outlier removal, and adding noise to the training data. It shows the loss and accuracy curves for the autoencoder model.

1. Bakhtiar Zaid Ariadji
23120310
Implementation of Anomaly
Detection in Geothermal Power
Plant Using Autoencoders

2. Scope
Overview - Topik dan
Objek
Metode

3. Overview - Topik
dan Objek

4. Overview - Autoencoder
Autoencoder adalah jenis unsupervised neural
network yang bertujuan untuk:
1. Menerima input dari dataset.
2. Compress data input ke latent-space representation.
3. Rekonstruksi data dari latent-space representation
ke output.
auto-encoder (AE) sering digunakan dalam deteksi
anomaly pada engineered system.
Terbagi menjadi 2 bagian utama:
• Encoder: menerima input data dan meng-compress
data ke latent-space.
• Decoder: mengambil data dari latent-space dan
merekonstruksinya menjadi serupa dengan data
input.
1. Lu, Chen, et al. "Fault diagnosis of rotary machinery
components using a stacked denoising autoencoder-
based health state identification." Signal
Processing 130 (2017): 377-388.
2. Zhao, Rui, et al. "Deep learning and its applications to
machine health monitoring." Mechanical Systems and
Signal Processing 115 (2019): 213-237.
3. Ko, Jin Uk, et al. "A new auto-encoder-based dynamic
threshold to reduce false alarm rate for anomaly
detection of steam turbines." Expert Systems with
Applications 189 (2022): 116094.
4. Lee, Geunbae, et al. "Unsupervised anomaly
detection of the gas turbine operation via
convolutional auto-encoder." 2020 IEEE International
Conference on Prognostics and Health Management
(ICPHM). IEEE, 2020.

5. Overview - Autoencoder
Ko, Jin Uk, et al. "A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly
detection of steam turbines." Expert Systems with Applications 189 (2022): 116094

6. Overview -
Autoencoder Dimensionality reduction
Denoising
Anomaly Detection
Tujuan penggunaan autoencoder adalah untuk
melatih sebuah network yang dapat merekonstruksi
data input, namun nilai utama dari autoencoder
adalah penggunaan latent-space:

7. Skema Autoencoder
Ko, Jin Uk, et al. "A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam
turbines." Expert Systems with Applications 189 (2022): 116094

8. Dataset
Feature Sensor Satuan Nama Feature
Turbine Inlet Steam Flow FI-201 ton/h tbnflow
Turbine Inlet Pressure PI-205 BarG tbnpres205
Turbine Inlet Temperature TI-203 °C tbntem203
Shaft Bearing Vibration X No. 1 VI-201A μm brg_x1
Shaft Bearing Vibration Y No. 1 VI-201B μm brg_y1
Shaft Bearing Vibration Y No. 2 VI-202B μm brg_y2
Generator Vibration X No. 1 VI-1101A μm gen_x1
Generator Vibration Y No. 1 VI-1101B μm gen_y1
Generator Vibration X No. 2 VI-1102A μm gen_x2
Generator Vibration Y No. 2 VI-1102B μm gen_y2

11. Preprocessing – Missing Value Treatment
Before After

12. Preprocessing – Outlier Removal
Before
After

13. Training Data –
Train & Test Split

14. Training Data –
Scaler & Noise
Generating

15. Modelling Data

16. Model Loss Curve Model Accuracy Curve

17. Terima Kasih