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