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

9.
Preprocessing – Missing Value Treatment
Before After

10.
Preprocessing – Outlier Removal
Before
After

11.
Training Data –
Train & Test Split

12.
Training Data –
Scaler & Noise
Generating

13.
Modelling Data

14.
Model Loss Curve Model Accuracy Curve

15.
Terima Kasih