Faulty Sensor Research Articles

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

SummaryNode faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts.

Feature engineering and deep learning-based approach for event detection in Medical Internet of Things (MIoT)

Medical Internet of Things (MIoT) is becoming increasingly mainstream as their applications continue to grow. These applications include remote healthcare monitoring, drug storage, alarm systems, and medical wearable devices. Remote patient monitoring, especially using wearable devices, has improved the health and protection of numerous people, while avoiding unnecessary hospital visits. Ideally, healthcare monitoring systems should accurately and timely detect if the patient is entering a critical condition, and alert the concerned doctor accordingly. However, in practice, there are two kinds of alarms raised by such systems, namely, events and anomalies. Events are non-erroneous data that do not conform to the expected values, indicating abnormality in the patient’s condition. On the other hand, anomalies are erroneous data that may result from faulty sensors, electromagnetic interference, malicious attacks, or hardware tampering. These anomalies lead to false alarms which affect the decision making process and may cause danger to the patient’s life. Traditionally, wearable MIoTs, measuring different vitals, frequently communicate with the patient’s phone, or a local processing unit (LPU), in order to analyze and process the collected data. However, the increase in processing capabilities of IoT devices has made on-device event detection possible. This adds an extra layer of defense while avoiding unnecessary communication with LPU. In this paper, a healthcare monitoring system is proposed which compromises of two layers - (1) an online lightweight approach, based on multivariate long-short term memory (LSTM) autoencoder, embedded in each MIoT, that is able to detect data abnormality and alarms the LPU, (2) a correlation technique, at the LPU, that differentiates between anomalies and events, and alerts the patient’s doctor only in case of an event. Since a single sensor reading does not provide enough clinical insight to detect the full length of an event, a feature engineering approach is implemented to extract interpretable statistical, dynamic and physiological features that provide the clinical insight needed. The proposed approach is simulated using Medical Information Mart for Intensive Care (MIMIC) dataset for various vital signs, such as heart rate, systolic and diastolic blood pressure. It is compared with four benchmarks, where the results show the robustness of the model in differentiating between anomalies and events, with event detection sensitivity and specificity above 93%.

Self-Diagnostic and Self-Compensation Methods for Resistive Displacement Sensors Tailored for In-Field Implementation.

This paper presents a suitably general model for resistive displacement sensors where the model parameters depend on the current sensor conditions, thereby capturing wearout and failure, and proposes a novel fault detection method that can be seamlessly applied during sensor operation, providing self-diagnostic capabilities. On the basis of the estimation of model parameters, an innovative self-compensation method is derived to increase the accuracy of sensors subject to progressive wearout. The proposed model and methods have been validated by both numerical simulations and experimental tests on two real resistive displacement sensors, placed in undamaged and faulty conditions, respectively. The fault detection method has shown an accuracy of 97.2%. The position estimation error is < ±0.2% of the full-scale span for the undamaged sensor, while the self-compensation method successfully reduces the position estimation error from ±15% to approximately ±2% of the full-scale span for the faulty sensor.

Improved solar photovoltaic performance in standalone low‐voltage direct current microgrids using sensor fault tolerant control

AbstractThe advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid.

AHU sensor fault diagnosis in various operating conditions based on a hybrid data-driven model combined energy consumption

Data-driven methods has the inspiring potential for sensors fault detection in heating, ventilation, and air-conditioning (HVAC) intelligence control system. However, the interpretability of pure data-driven methods is relatively poor. Moreover, pure data-driven methods heavily rely on quality of HVAC sensor data that causes pure data driven methods have inconsistent detection efficiency in various operation conditions and sensor locations, resulting in poor general applicability. To solve above problems, an enhanced data-driven model based on energy consumption correlation (ECC) is proposed. First, the collected data from sensors are collected in chronological order and undergo data preprocessing techniques for noise removal. Subsequently, the energy consumption data is utilized to differentiate the operating conditions of the HVAC system. Simultaneously, a mapping relationship is established between sensor data and energy consumption. It uses the mapping relationship to construct the ECC for faulty sensor detection. Finally, the diagnosis of air handling unit (AHU) sensor faults is achieved by employing the hybrid data-driven model based on the ECC. The experiment conducted a comparison between the hybrid data-driven model and two novel pure data-driven methods. The results of the experiment demonstrate that the hybrid data-driven model exhibits excellent fault detection efficiency across different operating conditions and sensor locations. The fault alarm rate of the hybrid data-driven model is 0–3.26% under various AHU sensor fault conditions. And the fault diagnosis rate of the hybrid data-driven model is 96.23% and 96.11% under single-operation condition and dual-operation condition, respectively. Furthermore, compared to pure data-driven methods, the hybrid data-driven model is less affected by data quality and possesses a broader applicability.

Identification of Faulty Sensor Nodes in WBAN Using Genetically Linked Artificial Neural Network

Wireless Body Area Networks (WBANs) have risen as a promising innovation for checking humanphysiological parameters in real time. Be that as it may, the unwavering quality and precision of WBANs dependon the right working of sensor hubs. The distinguishing proof of defective sensor hubs is vital for guaranteeing thequality of information collected by WBANs. In this paper, we propose a novel approach to recognizing faultysensor hubs in WBAN employing a hereditarily linked artificial neural organize (GLANN). The GLANN isprepared to employ a crossbreed fuzzy-genetic calculation to optimize its execution in distinguishing defectivesensor hubs. The proposed approach is assessed employing a dataset collected from a real-world WBAN. Thecomes about appears that the GLANN-based approach beats existing strategies in terms of exactness andproficiency. The proposed approach has potential applications within the field of healthcare, where exact and solidobserving of human physiological parameters is basic for conclusion and treatment. By and large, this ponderpresents a promising approach to progressing the unwavering quality and exactness of WBANs by identifyingflawed sensor hubs utilizing GLANN .

Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise, interactive effects of multiple faults and fault propagation. This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems. Actuator and sensor fault isolation are accomplished in two independent modules, that monitor the system and are able to isolate the potential faulty actuator(s) or sensor(s). For the sensor fault isolation (SFI) case, a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s). For the actuator fault isolation (AFI) case, a second module is designed, which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s) and, based on a reasoning decision logic and suitably designed AFI thresholds, the faulty actuator(s) set can be determined. The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks.

A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor's integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network's local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra's Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra's URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.

Research and implementation of fault data recovery method for dry-type transformer temperature control sensor based on ISSA-LSTM algorithm

This study proposes a temperature control sensor fault data recovery model based on the improved Sparrow Search Algorithm (ISSA) optimizing Long Short-Term Memory (LSTM) to address erroneous data output caused by temperature monitoring sensor failures in dry-type transformers. The model aims to recover faulty sensor data in dynamic processes. It enhances the algorithm by initializing the population using Tent chaotic mapping, employing t-distribution and differential variation perturbations, and introducing a dynamic step size factor. Simulation experiments evaluated the performance of ISSA-LSTM, SSA-LSTM, PSO-LSTM, GA-LSTM, and ACSA-LSTM algorithms. The ISSA-LSTM model outperforms the worst PSO-LSTM model by 82.6%, 82.1%, and 1.8% in terms of MAE, RMSE and R2 values, respectively. Field experiments confirmed the algorithm's effectiveness in recovering data from faulty sensors and different fault types, improving the accuracy and stability of temperature control sensor fault data recovery in dry-type transformers.

Deep-learning-based design of active fault-tolerant control for automated manufacturing systems subjected to faulty sensors

This research paper proposes a new implementation of a long short-term memory network for active fault-tolerant control subjected to single and multiple undiagnosable sensor faults. There are two networks used: the first performs as a diagnosis for automated manufacturing systems and can identify faulty sensors, while the second acts as an inverse model of these systems and is used to determine the reconfigured control action to take when sensors are not functioning as expected. The Factory I/O simulator is interfaced with MATLAB to simulate and verify the proposed approach for automated material handling case study with faultless and multiple fault sensors. Four sensors of the case study out of six are tolerated.

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data

Abstract The operation of smart wastewater treatment plants (WWTPs) is increasingly paramount in improving effluent quality, facilitating resource recovery and reducing carbon emissions. To achieve these objectives, sensors, monitoring systems, and artificial intelligence (AI)-based models are increasingly being developed and utilised for decision support and advanced control. Key to the adoption of advanced data-driven control of WWTPs is real-time data validation and reconciliation (DVR), especially for sensor data. This research demonstrates and evaluates real-time AI-based data quality control methods, i.e. long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models. The DVR procedure is aimed at anomalies resulting from data acquisition issues and sensor faults. Anomaly detection precedes the reconciliation procedure using models that capture short-time dynamics (SD) and (relatively) long-time dynamics (LD). Real data from an operational WWTP are used to test the DVR procedure. To address the reconciliation of prolonged anomalies, the SD is aggregated with an LD model by exponential weighting. For reconciling single-point anomalies, both ARIMA and LSTM AEs showed high accuracy, while the accuracy of reconciliation regresses quickly with increasing forecasting horizons for prolonged anomalous events.

Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records

The Chicago Array of Things (AoT) is a robust dataset taken from over 100 nodes over four years. Each node contains over a dozen sensors. The array contains a series of Internet of Things (IoT) devices with multiple heterogeneous sensors connected to a processing and storage backbone to collect data from across Chicago, IL, USA. The data collected include meteorological data such as temperature, humidity, and heat, as well as chemical data like CO2 concentration, PM2.5, and light intensity. The AoT sensor network is one of the largest open IoT systems available for researchers to utilize its data. Anomaly detection (AD) in IoT and sensor networks is an important tool to ensure that the ever-growing IoT ecosystem is protected from faulty data and sensors, as well as from attacking threats. Interestingly, an in-depth analysis of the Chicago AoT for anomaly detection is rare. Here, we study the viability of the Chicago AoT dataset to be used in anomaly detection by utilizing clustering techniques. We utilized K-Means, DBSCAN, and Hierarchical DBSCAN (H-DBSCAN) to determine the viability of labeling an unlabeled dataset at the sensor level. The results show that the clustering algorithm best suited for this task varies based on the density of the anomalous readings and the variability of the data points being clustered; however, at the sensor level, the K-Means algorithm, though simple, is better suited for the task of determining specific, at-a-glance anomalies than the more complex DBSCAN and HDBSCAN algorithms, though it comes with drawbacks.

Deep Reinforcement Learning Based Reliable Data Transmission Scheme for Internet of Underwater Things in 5G and Beyond Networks

The Internet of Underwater Things (IoUT) is an interconnected communication ecosystem for underwater devices in maritime environments. IoUT devices range from seabed sensors to ships and boats in oceans which help in diverse practical applications of underwater information ranging from marine transportation, navigation, and underwater exploration to disaster prevention with intelligent monitoring. There exist significant obstacles within the present architecture of the IoUT. This article proposes three distinct methodologies aimed at enhancing the robustness and reliability of the communication architecture for 5G and future networks. Firstly, the employment of the Deep Reinforcement Learning approach is advocated for the identification of anomalies and faulty sensors. Secondly, energy consumption is identified as a crucial factor for IoUT devices. To address this concern, the integration of renewable energy sources, namely oxygen rechargeable batteries, with nano-generators is employed. This integration allows for the replenishment of the batteries through the harnessing of kinetic energy from water. Thirdly, the process by which information can be efficiently transmitted from IoUT sensors located on the seabed to the final terrestrial network is delineated, with an emphasis on minimizing the number of hops required. Additionally, a case study is presented, focusing on the mitigation of interference within the IoUT. Lastly, the article delves into the various research challenges and unresolved matters that persist within the realm of IoUT.

Analysis of Troubleshooting Effect on the Performance of Gas Engine G-102 B (TBG620 V16K) in Central Processing Area of PT. XYZ

PT. XYZ is a part of the largest oil and Gas Company in Indonesia, responsible for managing and developing oil and gas production activities in the East Java. The Central Processing Area serves as the facility for processing fluids produced from the East Java fields. The processed output consists of three phases: Gas, Oil, and Water. The operation of the Gas Engine G102-B TBG620 V16K running 24 hours a day to generate electricity at the central processing area of PT. XYZ requires regular maintenance, including scheduled preventive maintenance and unplanned or corrective maintenance. In the event of troubleshooting, such as a decrease in temperature in chamber 2B or a temperature sensor reading of 280 °C in that chamber, immediate corrective maintenance is necessary. Based on observations conducted in the power plant area, two possibilities causing the temperature decrease in that chamber were identified. The first possibility was the need for spark plug replacement, and the second was a malfunctioning temperature sensor resulting in incorrect readings. Therefore, the spark plug replacement was performed first, requiring a gas engine shutdown for approximately 2 hours for repairs. After the spark plug replacement, it was discovered that there was no change in the temperature reading in chamber 2B, which remained at 280 °C. Hence, the corrective maintenance performed did not have a significant impact because the issue was caused by a faulty temperature sensor. However, it was observed that there was a 1.16% increase in the generated power compared to before the corrective maintenance was conducted. The difference in power output could be attributed to variations in the quality of gas used in the combustion process of the gas engine.

Advancing Sensor Data Integrity with Deep Learning-Based Fault Detection

In the realm of modern data-driven systems, accurate and reliable sensor measurements are imperative for informed decision-making and system integrity. The objective of this project is to develop a robust sensor fault detection methodology leveraging the power of deep learning techniques. The ubiquity of sensors such as temperature and humidity sensors has led to a critical need for discerning between accurate readings and erroneous data, thereby enhancing the reliability of these measurements. This study addresses the prevailing challenge of sensor reliability by introducing a data-driven approach that harnesses deep learning algorithms to detect sensor faults promptly and accurately. A comprehensive dataset comprising sensor readings under diverse conditions forms the foundation of this investigation. Multiple cutting-edge deep learning architectures and techniques are systematically explored and evaluated against this dataset to identify the most efficient and precise sensor fault detection method. The outcomes of this research contribute significantly to advancing the state-of-the-art in sensor fault detection, with implications for a wide array of applications reliant on sensor measurements. By harnessing the capabilities of deep learning, this project presents a tangible solution to the challenge of accurately identifying faulty sensor data in real-time, thereby bolstering the dependability and efficacy of sensor-driven systems. Ultimately, this work underscores the potential of integrating advanced machine learning techniques in ensuring the reliability and precision of sensor data, heralding a new era of robust and trustworthy sensor-enabled environments.

Current Sensor Fault Diagnosis of DFIG Wind Turbines Using an Extended Kalman Filter Observer: Experimental Validation

This article deals with the diagnosis of faults in rotor current sensors in wind systems based on the Doubly Fed Induction Generator (DFIG). An approach is developed to monitor rotor currents and identify faulty sensors, even during variable wind speeds. This method relies on the use of the Extended Kalman Filter (EKF) observer, which is based on the nonlinear model of the DFIG and a fault identification block in the abc reference frame. Firstly, the EKF estimates the rotor currents from stator current and mechanical speed measurements. Then, these estimates are processed in the localization block to precisely identify faulty sensors, whether it be single or multiple faults. This method has been implemented on a FPGA and has been experimentally validated. The experimental results confirm the effectiveness of this approach in monitoring rotor currents and identifying faulty sensors, in the case of single or multiple faults.

Faulty sensor signal reconstruction in Li-ion battery packs

Faulty sensor signal reconstruction in Li-ion battery packs

Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features

Sensor fault diagnosis is essential to guaranteeing the safety of lithium-ion batteries. To address the general drawbacks of the existing diagnosis methods, including the difficulty in determining the threshold, inability to handle multiple faulty sensors concurrently, and limited capacity in identifying fault modes, a multi-sensor multi-mode fault diagnosis method for lithium-ion battery packs is proposed. The proposed method utilizes time series and discriminative features to accomplish sensor-specific fault detection and fault mode identification. First, a total of 18 general time series features are extracted to characterize the measurements of each sensor during each charge–discharge cycle. Principal component analysis is then used to reduce the high-dimensional feature space to a two-dimensional space, such that fault detection can be carried out with the α-hull algorithm. For the detected faulty samples, a two-layer identification algorithm is designed based on three discriminative features, namely, correlation coefficient, impulse factor, and Hurst coefficient, to identify the specific fault modes. The diagnostics can decouple the information from different types of sensors so that the proposed method can effortlessly isolate current, voltage, and temperature sensors that are concurrently experiencing faults. Ultimately, experimental results from three scenarios, including simultaneous failure of multiple sensors, substantiate the effectiveness and feasibility of the proposed method.

Nonsingleton Gaussian type-3 fuzzy system with fractional order NTSMC for path tracking of autonomous cars

Path-tracking and lane-keeping tasks are critical to guarantee safety and navigation performance considerations for deploying autonomous cars. This paper presents a novel control framework for the path-tracking control of high-speed autonomous cars with structured uncertainties. This study introduces a nonlinear adaptive control system based on a fractional-order terminal sliding mode system while incorporating a novel Gaussian Nonsingleton type-3 fuzzy system (FOTSM-NT3FS). Therefore, the proposed controller is independent of the information about the ego vehicle’s dynamic information, and instead, the dynamics are approximated through a developed NT3FLS. The developed control system exhibits robustness to measurement errors and faulty sensors, because the inputs to the NT3FS are uncertain. In order to guarantee the boundedness of the adaptation parameters, the σ−mod approach is employed. The Lyapunov stability theorem and Barbalat’s lemma are used to ensure the uniform ultimate boundedness of the closed loop system and the convergence of tracking errors to the origin in finite time. High-fidelity co-simulations with CarSim and MATLAB are performed to verify the effectiveness of the proposed control scheme and are also compared to other reported methods in the literature. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path-tracking tasks and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.