Simulation of Virtual Redundant Sensor Models for Safety-Related Applications

Peniak, Peter; Rástočný, Karol; Kanalikova, Alzbeta; Bubeníková, Emília

doi:10.3390/s22030778

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…In the ideal case, both calibration parameters are zero. If we invert (20) and compare it with (7), the gain and bias of j-th sensor G j , B j from (7) can be rewritten in terms of c 1j , c 2j :…”

Section: Homogeneous Sensor Fusionmentioning

confidence: 99%

“…The estimated vertical direction from the accelerometer can be combined with the gyroscope readings using various sensor fusion methods, improving the precision of the estimated angular velocity and Euler angles (see, e.g., [ 8 , 9 , 15 , 16 , 17 , 18 , 19 ]). Using multiple sensors also improves the safety of the whole system (see, e.g., [ 20 , 21 ]). To estimate the position of the object, the accelerometer and gyroscope readings can be combined with the odometrical data [ 22 ], microwave range finder [ 23 ], or laser scanner [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Nemec

Andel

Šimák

et al. 2023

Sensors

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The article deals with sensor fusion and real-time calibration in a homogeneous inertial sensor array. The proposed method allows for both estimating the sensors’ calibration constants (i.e., gain and bias) in real-time and automatically suppressing degraded sensors while keeping the overall precision of the estimation. The weight of the sensor is adaptively adjusted according to the RMSE concerning the weighted average of all sensors. The estimated angular velocity was compared with a reference (ground truth) value obtained using a tactical-grade fiber-optic gyroscope. We have experimented with low-cost MEMS gyroscopes, but the proposed method can be applied to basically any sensor array.

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Section: Homogeneous Sensor Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Nemec

Andel

Šimák

et al. 2023

Sensors

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“…The MQTTset was generated from a real-time IoT network with eight IoT sensors connected to an MQTT broker, using the IoT-Flock tool [43]. It comprises five distinct 1 Access at: https://ieee-dataport.org/documents/edge-iiotset-newcomprehensive-realistic-cyber-security-dataset-iot-and-iiot-applications 2 Access at: https://www.kaggle.com/datasets/cnrieiit/mqttset imp_v ← XGBoost(F, Y ) ▷ compute impact values imp_v of features 8:…”

Section: A Datasetmentioning

confidence: 99%

“…T HE Industrial Internet of Things (IIoT) is a network of interconnected devices, sensors, and equipment used in industrial settings to collect, exchange, and analyze data. IIoT aims to enhance operational efficiency, enable predictive maintenance, optimize processes, and improve overall productivity in industries such as manufacturing, energy, transportation, and healthcare [1], [2]. Several IIoT devices are operating with sensitive data, and the growing prevalence of IIoT in industrial environments also amplifies the potential for security breaches and compromises [3]- [5].…”

mentioning

confidence: 99%

MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion Detection in IIoT Networks

Ullah,

Boulila,

Koubâa

et al. 2023

IEEE Access

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The increasing prevalence of the Industrial Internet of Things (IIoT) in industrial environments amplifies the potential for security breaches and compromises. To monitor IIoT networks, intrusion detection systems (IDS) have been introduced to detect malicious activities within the network flow, in which machine learning (ML) and deep learning (DL) play an important role. However, existing IDSs face challenges during training when dealing with imbalanced training data and a higher number of classes. These issues can significantly reduce the IDS's performance and may result in missed network attacks, especially those with fewer training samples. To address these challenges, this paper introduces a multi-head attention-based gated recurrent unit (MAGRU) that scrutinizes IIoT network traffic to detect malicious activities. In the proposed model, the multi-head attention (MA) has the ability to enhance the learning capability of the model to handle limited sample classes. The gated recurrent unit (GRU) is employed for the detection of IIoT network behavior. The proposed MAGRU is evaluated using two publicly available datasets, namely Edge-IIoTset and MQTTset. To validate the performance of the proposed MAGRU, various ML and DL models were implemented and compared against MAGRU using the same dataset. The proposed model outperformed the other models, achieving an average precision, recall, F1-score, and accuracy of 99.62%, 99.67%, 99.64%, and 99.97%, respectively, for the aforementioned datasets. These results demonstrate optimal performance in the detection of intrusions in IIoT networks.

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“…In particular, IEC 61508 defines through ISO 26262 [ 5 ] a series of standards that are dedicated to automotive components and are used in a more general context of functional safety to suppress and eliminate undue risks caused by improper behavior of electrical/electronic systems. Recent publications include [ 6 ], in which redundant virtual sensors were applied to guarantee continuity in service. In [ 7 ], the dual concept of virtual sensors using EKFs was used to maintain continuity of service in DC/DC systems.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

Schimmack

Belda

Mercorelli

2023

Sensors

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This paper deals with a specific approach to fault detection in transformer systems using the extended Kalman filter (EKF). Specific faults are investigated in power lines where a transformer is connected and only the primary electrical quantities, input voltage, and current are measured. Faults can occur in either the primary or secondary winding of the transformer. Two EKFs are proposed for fault detection. The first EKF estimates the voltage, current, and electrical load resistance of the secondary winding using measurements of the primary winding. The model of the transformer used is known as mutual inductance. For a short circuit in the secondary winding, the observer generates a signal indicating a fault. The second EKF is designed for harmonic detection and estimates the amplitude and frequency of the primary winding voltage. This contribution focuses on mathematical methods useful for galvanic decoupled soft sensing and fault detection. Moreover, the contribution emphasizes how EKF observers play a key role in the context of sensor fusion, which is characterized by merging multiple lines of information in an accurate conceptualization of data and their reconciliation with the measurements. Simulations demonstrate the efficiency of the fault detection using EKF observers.

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Simulation of Virtual Redundant Sensor Models for Safety-Related Applications

Cited by 7 publications

References 22 publications

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion Detection in IIoT Networks

Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

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