4.6 Article

Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference

Related references

Note: Only part of the references are listed.
Article Construction & Building Technology

Improvement of virtual in-situ calibration in air handling unit using data preprocessing based on Gaussian mixture model

Tianyi Zhao et al.

Summary: This study proposes a method that uses Gaussian mixture model to preprocess historical data in order to address the uncertainty issue in calibration results caused by the dynamic nature of operating conditions in air conditioning systems. By applying the VBEM algorithm and EM algorithm to cluster and solve the GMM, the clustering results are incorporated into a virtual in-situ calibration method, leading to improved accuracy of the sensors.

ENERGY AND BUILDINGS (2022)

Article Computer Science, Interdisciplinary Applications

Generally weighted moving average control chart for monitoring two-parameter exponential distribution with measurement errors

Qi Li et al.

Summary: A GWMA-M control chart for two-parameter exponential distributions is proposed for efficient detection of small shifts and handling measurement errors, showing advantages and efficiency in comparison with other existing charts in the presence of measurement errors.

COMPUTERS & INDUSTRIAL ENGINEERING (2022)

Article Construction & Building Technology

A study on the sensor calibration method using data-driven prediction in VAV terminal unit

Hyo-Jun Kim et al.

Summary: A sensor calibration method using data-driven prediction models was developed to eliminate sensor errors in VAV systems. Performance evaluations showed the method effectively corrected errors and could handle both single and multiple errors. The calibration of sensor data also addressed practical challenges such as sensor replacement.

ENERGY AND BUILDINGS (2022)

Article Acoustics

Markov Chain Monte Carlo-based Bayesian method for nonlinear stochastic model updating

Ya-Jie Ding et al.

Summary: This study proposed a Markov Chain Monte Carlo-based Bayesian method for updating nonlinear stochastic models, utilizing instantaneous characteristics. By employing the discrete mode decomposition method and Bayesian theorem, the sampling efficiency was enhanced through calculation of the posterior probability density function.

JOURNAL OF SOUND AND VIBRATION (2022)

Article Engineering, Mechanical

CNN and Convolutional Autoencoder (CAE) based real-time sensor fault detection, localization, and correction

Debasish Jana et al.

Summary: This research introduces a novel deep learning framework for identifying faults in sensor data, locating the faulty sensors, and reconstructing the correct sensor data. The framework performs well in both single and multiple sensor faults and demonstrates high computational efficiency.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Article Chemistry, Analytical

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Huajun Bai et al.

Summary: It is crucial to use wireless data transmission technologies for remote and real-time monitoring of diesel engine health state. This study proposes a method for compression reconstruction and fault diagnostics of diesel engine vibration data based on sparse Bayesian optimization block learning. By combining compressive sensing technology with fault diagnosis, this method improves wireless transmission efficiency and achieves better diagnostic effects.

SENSORS (2022)

Article Chemistry, Analytical

On-line drift compensation for continuous monitoring with arrays of cross-sensitive chemical sensors

Sudip Paul et al.

Summary: In this paper, a multi-calibration ensemble approach is proposed to compensate for sensor drift in long-term application of chemical sensor arrays. The method utilizes past sensor measurements and known ground-truth data to build a regression model for predicting the concentration of target analytes. Experimental and simulation results demonstrate the superiority of the proposed approach compared to existing methods under various conditions.

SENSORS AND ACTUATORS B-CHEMICAL (2022)

Article Construction & Building Technology

Study of thermal sensation prediction model based on support vector classification (SVC) algorithm with data preprocessing

Tingzhang Liu et al.

Summary: This study verifies the potential of using the combined ENN+SMOTE+SVC method to predict thermal sensation vote (TSV), and demonstrates that this model achieves better performance than PMV index and other classic classification algorithms.

JOURNAL OF BUILDING ENGINEERING (2022)

Article Biotechnology & Applied Microbiology

Prediction model of ecological environmental water demand based on big data analysis

Lihong Zhao

Summary: The study constructed a prediction model of eco-environmental water demand using big data analysis and principal component analysis to select auxiliary variables. By analyzing basic water demand, leakage water demand, and water surface evaporation eco-environmental water demand, the model successfully predicted water demand. Experimental results showed a prediction error within 19.3 compared to existing models, demonstrating better prediction effectiveness.

ENVIRONMENTAL TECHNOLOGY & INNOVATION (2021)

Article Construction & Building Technology

A novel fault diagnosis and self-calibration method for air-handling units using Bayesian Inference and virtual sensing

Zhiqiang Liu et al.

Summary: This study introduced a novel fault detection, diagnosis, and self-calibration method based on Bayesian inference and virtual sensing to estimate various faults, including sensor and component faults. The method effectively recognized system operating state and identified fault positions, reducing deviation rate by up to 98.0% in most fault scenarios.

ENERGY AND BUILDINGS (2021)

Article Chemistry, Analytical

Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory

Tianhong Gao et al.

Summary: This paper introduces a data-driven method for predicting the remaining useful life (RUL) of bearings based on Bayesian theory. Time-domain features are extracted from bearing vibration signals and a Bayesian model is established to predict RUL with high accuracy.

SENSORS (2021)

Article Engineering, Electrical & Electronic

Electricity theft detection based on stacked sparse denoising autoencoder

Yifan Huang et al.

Summary: This paper presents a method for detecting electricity theft based on autoencoders, using a stacked sparse denoising autoencoder approach. The technique trains on electricity data from honest users, reconstructs inputs, and identifies theft users by setting an appropriate error threshold. Sparsity, noise, and particle swarm optimization are used to improve feature extraction and robustness, with the proposed approach evaluated on a electricity dataset in Fujian, China.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

Article Chemistry, Analytical

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

Toyosi Ademujimi et al.

Summary: This paper introduces a fusion-learning method for Bayesian Networks (BNs) to improve fault diagnosis, utilizing both quantitative and qualitative data sources, resulting in better diagnostic outcomes with wider fault coverage compared to individual BN models.

SENSORS (2021)

Article Construction & Building Technology

An improved stacking ensemble learning-based sensor fault detection method for building energy systems using fault-discrimination information

Guannan Li et al.

Summary: The study proposed an improved Stacking sensor fault detection method using fault-discrimination information, employing ensemble learning with four single models to achieve better fault detection performance and lower false-alarm rate compared to traditional Stacking methods.

JOURNAL OF BUILDING ENGINEERING (2021)

Review Thermodynamics

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

Cheng Fan et al.

Summary: Buildings play a significant role in global sustainability, and data-driven research methods have greatly enriched knowledge in building energy modeling and improved building performance. With the ongoing development of smart buildings and IoT-driven smart cities, big data-driven research paradigm is becoming an essential complement to existing scientific research methods in the building sector.

BUILDING SIMULATION (2021)

Article Construction & Building Technology

In -situ sensor calibration in an operational air -handling unit coupling autoencoder and Bayesian inference

Sungmin Yoon

ENERGY AND BUILDINGS (2020)

Article Construction & Building Technology

Virtual sensor-assisted in situ sensor calibration in operational HVAC systems

Youngwoong Choi et al.

BUILDING AND ENVIRONMENT (2020)

Article Thermodynamics

Wind speed sensor calibration in thermal power plant using Bayesian inference

Ali Mokhtari et al.

CASE STUDIES IN THERMAL ENGINEERING (2020)

Article Construction & Building Technology

Bayesian method for HVAC plant sensor fault detection and diagnosis

K. H. Ng et al.

ENERGY AND BUILDINGS (2020)

Article Thermodynamics

A novel deep reinforcement learning based methodology for short-term HVAC system energy consumption prediction

Tao Liu et al.

INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID (2019)

Article Automation & Control Systems

Data-driven compensation method for sensor drift faults in digital PID systems with unknown dynamics

Jun-Sheng Wang et al.

JOURNAL OF PROCESS CONTROL (2018)

Article Mathematics, Applied

A novel gray forecasting model based on the box plot for small manufacturing data sets

Che-Jung Chang et al.

APPLIED MATHEMATICS AND COMPUTATION (2015)

Article Construction & Building Technology

Virtual in-situ calibration method in building systems

Yuebin Yu et al.

AUTOMATION IN CONSTRUCTION (2015)

Article Engineering, Electrical & Electronic

Drift removal by means of alternating least squares with application to Herschel data

Lorenzo Piazzo et al.

SIGNAL PROCESSING (2015)

Article Construction & Building Technology

Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units

Ken Bruton et al.

AUTOMATION IN CONSTRUCTION (2014)

Article Construction & Building Technology

Preventive approach to determine sensor importance and maintenance requirements

Li Zhengwei et al.

AUTOMATION IN CONSTRUCTION (2013)