Enhanced Reliability of ADAS Sensors Based on the Observation of the Power Supply Current and Neural Network Application

Grzechca, Damian; Ziębiński, Adam; Rybka, Paweł

doi:10.1007/978-3-319-67077-5_21

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…For example, current signature analysis is used i induction motors to provide valuable information on bottle capping failures [10]. Suppl current analysis is also used in the automotive industry for examining the reliability o advanced driver-assistance systems (ADAS) modules [11].…”

Section: Introductionmentioning

confidence: 99%

“…Barrier machine design and functional requirements differ depending on the country of application. For the purpose of this research, requirements valid for two countries, i.e., Poland [9,10] and the United Kingdom [11] were taken into consideration. One of the important factors that has an impact on the testing scenario is the range of the angular movement of the boom.…”

Section: Introductionmentioning

confidence: 99%

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Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis

2021

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Barrier machines are a key component of automatic level crossing systems ensuring safety on railroad crossings. Their failure results not only in delayed railway transportation, but also puts human life at risk. To prevent faults in this critical safety element of automatic level crossing systems, it is recommended that fault and anomaly detection algorithms be implemented. Both algorithms are important in terms of safety (information on whether a barrier boom has been lifted/lowered as required) and predictive maintenance (information about the condition of the mechanical components). Here, the authors propose fault models for barrier machine fault and anomaly detection procedures based on current waveform observation. Several algorithms were applied and then assessed such as self-organising maps (SOM), autoencoder artificial neural network, local outlier factor (LOF) and isolation forest. The advantage of the proposed solution is there is no change of hardware, which is already homologated, and the use of the existing sensors (in a current measurement module). The methods under evaluation demonstrated acceptable rates of detection accuracy of the simulated faults, thereby enabling a practical application at the test stage.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis

2021

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“…The movement of vehicles close to each other with high speed influences the appearance of additional dangerous factors. Therefore, the use of a data fusion algorithm [ 10 , 11 ] that will take into account the measurement of speed [ 12 ] and distance to the preceding car and other significant factors such as recognized road signs [ 13 , 14 ], road infrastructure [ 15 ], and driver [ 16 ] or system behavior is essential [ 17 ]. Additionally, the time to retrieve information from the environment by sensors and fast processing of those data is important.…”

Section: Introductionmentioning

confidence: 99%

How Accurate Can UWB and Dead Reckoning Positioning Systems Be? Comparison to SLAM Using the RPLidar System

Grzechca

Ziębiński

Paszek

et al. 2020

Sensors

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This paper compares two positioning systems, namely ultra-wideband (UWB) based micro-location technology and dead reckoning and a RPLidar based simultaneous localization and mapping (SLAM) solution. This new approach can be used to improve the quality of the positioning system and increase the functionality of advanced driver assistance systems (ADAS). This is achieved by using stationary nodes and UWB tags on the vehicles. Thus, the redundancy of localization can be achieved by this approach, e.g., as a backup to onboard sensors like RPlidar or radar. Additionally, UWB based micro-location allows additional data channels to be used for communication purposes. Furthermore, it is shown that the regular use of correction data increases UWB and dead reckoning accuracy. These correction data can be based on onboard sensors. This shows that it is promising to develop a system that fuses onboard sensors and micro-localization for safety-critical tasks like the platooning of commercial vehicles.

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“…Such a versatile quality-monitoring tool has to be based on a set of information that would describe the current state of the sensor without interfering with its inner structure. This can be done by examining the values of the module's temperature in particular spots, analysing the supply current waveforms [8] for certain voltage levels or measuring the timing and noises on the communication line.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Accuracy of ADAS Module Readings Based on an Analysis of the Transient Supply Current and Neural Network Application

Grzechca¹,

Rybka²,

Paszek³

2018

ElAEE

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The following paper presents advanced methods for evaluating the reliability of ADAS module readings, based on an analysis of the transient supply current. Changes in the transient current waveform occur due to environmental conditions and damage to a module's inner circuitry. Specific deviations in the waveforms may indicate a certain eventeither internal or external. This paper presents how to successfully distinguish certain anomalies using artificial neural network-based classification algorithms without having to interfere with the module's internal circuitry.

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Enhanced Reliability of ADAS Sensors Based on the Observation of the Power Supply Current and Neural Network Application

Cited by 11 publications

References 20 publications

Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis

Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis

How Accurate Can UWB and Dead Reckoning Positioning Systems Be? Comparison to SLAM Using the RPLidar System

Evaluation of the Accuracy of ADAS Module Readings Based on an Analysis of the Transient Supply Current and Neural Network Application

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