Comprehensive Survey on Time Series Analysis in Compressor-Based Machines

Core Concepts

Compressor-based machines offer rich time series data for fault detection, prediction, and forecasting, driving research in the field.

Abstract

• Compressor-based machines are crucial in various sectors, including refrigeration, HVAC, and heat pumps.
• IoT data collection enables proactive management and fault prediction in these machines.
• The survey focuses on Fault Detection (FD), Fault Prediction (FP), Forecasting, and Change Point Detection (CPD).
• Various algorithms and approaches are compared for each task, highlighting the importance of feature selection and model performance.
• Challenges include dataset availability, feature extraction, and algorithm selection.

Stats

"The work in [6] uses XGBoost to predict faults in data collected by IoT devices in refrigerators."
"The work in [65] aims at predicting faults in chillers in multi-storey buildings based on sensor alarms."
"The work in [36] predicts faults in chillers of commercial buildings using an autoencoder and classifier combination."

Quotes

"The vast body of knowledge and literature in the field demands a more systematic procedure for comparing new approaches with the previous works, which is essential for appraising the progress of research."
"DL methods are less used (19.13% comparisons in total), notwithstanding their good performances in [85, 88, 121]."

Key Insights Distilled From

Time Series Analysis in Compressor-Based Machines

by Fran... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17802.pdf

Deeper Inquiries

질문 1

압축기 기반 기계의 결함 예측을 위한 공개 데이터셋 부족 문제를 해결하는 방법은 무엇인가요?
답변 1 여기에

질문 2

실제 응용 프로그램에서 모델 훈련을 위해 합성 데이터를 사용하는 것의 영향은 무엇인가요?
답변 2 여기에

질문 3

그래프 신경망(Graph Neural Networks)과 물리적 지식을 활용한 신경망(Physics-Informed Neural Networks)을 압축기 기반 기계의 결함 예측에 통합하는 것이 어떻게 결함 예측을 향상시킬 수 있을까요?
답변 3 여기에