# Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization

arXiv: https://arxiv.org/abs/2301.04049 · HTML: https://mlsanity.com/paper/2301.04049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 68 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Reinforcement Learning for UAV Attitude Control](https://mlsanity.com/paper/1804.04154/) (2018)
- [Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning](https://mlsanity.com/paper/2002.02667/) (2020)
- [Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning](https://mlsanity.com/paper/1710.08904/) (2017)
- [Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning](https://mlsanity.com/paper/2106.09357/) (2021)
- [Network Intrusion Detection with Limited Labeled Data Using Self-supervision](https://mlsanity.com/paper/2209.03147/) (2022)
- [DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System](https://mlsanity.com/paper/2210.06399/) (2022)

## Abstract (arXiv)

There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems. We are searching for the most trustworthy and practical data-based fault detection methods proposed by artificial intelligence applications. In this paper, we propose a framework for fault detection based on reinforcement learning and a policy known as proximal policy optimization. As a result of the lack of fault data, one of the significant problems with the traditional policy is its weakness in detecting fault classes, which was addressed by changing the cost function. Using modified Proximal Policy Optimization, we can increase performance, overcome data imbalance, and better predict future faults. When our modified policy is implemented, all evaluation metrics will increase by 3% to 4% as compared to the traditional policy in the first benchmark, between 20% and 55% in the second benchmark, and between 6% and 14% in the third benchmark, as well as an improvement in performance and prediction speed compared to previous methods.