Sensor Drift Detection Based on Discrete Wavelet Transform and Grey Models

Han, Xiaoqing; Jiang, Jin; Xu, Aidong; Bari, Ataul; Pei, Chao; Sun, Youwen

doi:10.1109/access.2020.3037117

Cited by 15 publications

(3 citation statements)

References 33 publications

(56 reference statements)

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“…(Zhu et al, 2021) separate between self-detection of faults, proposing Least Square Support Vector Machine and back propagation neural network, self-identification of faults from historical data using wavelet packet decomposition for feature extraction, then decision tree algorithms and back propagation neural networks for pattern recognition and classification. (Han et al, 2020) proposes discrete wavelet transforms and grey models for detecting sensor drift, but do not propose any method for discerning between a trend in the measurand itself or an instrument drift. Other methods have been proposed, for example Bayesian calibration of sensor systems (Tancev & Toro, 2022).…”

Section: Neural Network and Machine Learningmentioning

confidence: 99%

Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors

et al. 2023

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In this paper we give an overview of factors and limitations impairing deep-sea sensor data, and we show how automatic tests can give sensors self-validation and self-diagnostic capabilities. This work is intended to lay a basis for sophisticated use of smart sensors in long-term autonomous operation in remote deep-sea locations. Deep-sea observation relies on data from sensors operating in remote, harsh environments which may affect sensor output if uncorrected. In addition to the environmental impact, sensors are subject to limitations regarding power, communication, and limitations on recalibration. To obtain long-term measurements of larger deep-sea areas, fixed platform sensors on the ocean floor may be deployed for several years. As for any observation systems, data collected by deep-sea observation equipment are of limited use if the quality or accuracy (closeness of agreement between the measurement and the true value) is not known. If data from a faulty sensor are used directly, this may result in an erroneous understanding of deep water conditions, or important changes or conditions may not be detected. Faulty sensor data may significantly weaken the overall quality of the combined data from several sensors or any derived model. This is particularly an issue for wireless sensor networks covering large areas, where the overall measurement performance of the network is highly dependent on the data quality from individual sensors. Existing quality control manuals and initiatives for best practice typically recommend a selection of (near) real-time automated checks. These are mostly limited to basic and straight forward verification of metadata and data format, and data value or transition checks against pre-defined thresholds. Delayed-mode inspection is often recommended before a final data quality stamp is assigned.

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Section: Neural Network and Machine Learningmentioning

confidence: 99%

Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors

et al. 2023

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“…where f dr (t) represents drift fault and n(t) is an irregular bounded disturbance signal, which is a sensor noise (due to the influence of external environment and internal factors of the sensor) [26].…”

Section: Real Valuementioning

confidence: 99%

Data-Driven State Prediction and Sensor Fault Diagnosis for Multi-Agent Systems with Application to a Twin Rotational Inverted Pendulum

Xin

Liu

et al. 2021

Processes

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When a multi-agent system is subjected to faults, it is necessary to detect and classify the faults in time. This paper is motivated to propose a data-driven state prediction and sensor fault classification technique. Firstly, neural network-based state prediction model is trained through historical input and output data of the system. Then, the trained model is implemented to the real-time system to predict the system state and output in absence of fault. By comparing the predicted healthy output and the measured output, which can be abnormal in case of sensor faults, a residual signal can be generated. When a sensor fault occurs, the residual signal exceeds the threshold, a fault classification technique is triggered to distinguish fault types. Finally, the designed data-driven state prediction and fault classification algorithms are verified through a twin rotational inverted pendulum system with leader-follower mechanism.

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“…For example, the discrete wavelet transform (DWT) has been shown to be an effective tool for eliminating clinical motion artifacts in thoracic electrical impedance tomography, including common artifacts like baseline drift [10]. A sensor drift detection method based on the DWT and the grey model has been demonstrated to be effective both in analog temperature sensor output data from a continuous stirred-tank reactor sensor model and in measurements from a physical temperature sensor at a nuclear power control test facility [11]. Empirical mode decomposition (EMD), which offers a higher degree of adaptability compared to wavelet variation, has also been shown to be effective in removing power line interference and baseline wander noise from electrocardiogram signals [12].…”

Section: Introductionmentioning

confidence: 99%

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

Wang,

Zhang,

et al. 2024

Applied Sciences

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Strain measurements are vital in engineering trials, testing, and scientific research. In the process of signal acquisition, baseline drift has a significant impact on the accuracy and validity of data. Traditional solutions, such as discrete wavelet transform and empirical mode decomposition, cannot be used in real-time systems. To solve this problem, this paper proposes a Transformer-based model to eliminate the drift in the signal. A self-attentive mechanism is utilized in the encoder of the model to learn the interrelationships between the components of the input signal, and captures the key features. Then, the decoder generates a corrected signal. Meanwhile, a high-precision strain acquisition system is constructed. The experiments tested the model’s ability to remove drift from simulated voltage signals with and without Gaussian noise. The results demonstrated that the transformer model excels at eliminating signal baseline drift. Additionally, the performance of the model was investigated under different temperature conditions and with different levels of force applied by the electronic universal testing machine to produce strain. The experimental results indicate that the Transformer model can largely eliminate drift in dynamic signals l and has great potential for practical applications.

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Sensor Drift Detection Based on Discrete Wavelet Transform and Grey Models

Cited by 15 publications

References 33 publications

Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors

Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors

Data-Driven State Prediction and Sensor Fault Diagnosis for Multi-Agent Systems with Application to a Twin Rotational Inverted Pendulum

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

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