Damage diagnosis in intelligent tires using time-domain and frequency-domain analysis

Behroozinia, Pooya; Khaleghian, Seyedmeysam; Taheri, Saied; Mirzaeifar, Reza

doi:10.1080/15397734.2018.1496842

Cited by 19 publications

(16 citation statements)

References 21 publications

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“…In contrast, the tire tread wear is typically visually inspected by drivers or measured using a gauge. To automatically detect tire damage or elements, various sensors are installed on tires [16], [17], [18], [19], [20]. Additionally, cameras are used to automatically detect tire wear or damage [21], [22]; this research indicated that more complex sensor systems and algorithms must be used to monitor wear.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Tire Tread Wear Detection Using Machine Learning in Real-Road Driving Conditions

et al. 2023

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The accurate detection of tire tread wear plays an important role in preventing tire-related accidents. In previous studies, tire wear detection is performed by interpreting mathematical models and tire characteristics. However, this approach may not accurately reflect the real driving environment. In this study, we propose a tire tread wear detection system that utilizes machine learning to provide accurate results under real-road driving conditions. The proposed system comprises (1) an intelligent tire that samples the measured acceleration signals and processes them in a dataset, (2) a preprocessing component that extracts features from the collected data according to the degree of wear, and (3) a detection component that uses a deep neural network to classify the degree of wear. To implement the proposed system in a vehicle, we designed an acceleration-based intelligent tire that can transmit data over wireless networks. At speeds between 30 and 80 km/h, the proposed system was experimentally demonstrated to achieve an accuracy of 95.51% for detecting tire tread wear under real-road driving conditions. Moreover, this system uses only preprocessed acceleration signals and machine-learning algorithms, without requiring complex physical models and numerical analyses.

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Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Tire Tread Wear Detection Using Machine Learning in Real-Road Driving Conditions

et al. 2023

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“…Traditional tire defect detection methods [12][13][14][15][16][17][18][19][20][21], which have been widely studied, can be divided into statistics-based methods [12][13][14][15], frequency domain analysis-based methods [16][17][18][19] and model-based methods [20,21]. Zhao and Qin proposed a feature mapping method for tire defect detection based on local inverse difference moment using gray-level cooccurrence matrix [12].…”

Section: Introductionmentioning

confidence: 99%

“…Behroozinia et al applied time and frequency domain analysis to infer the location and length information of cracks by comparing the differences between signals of defective and defect-free tire images [16]. Wavelet multi-scale analysis was used to distinguish the edges of defect and background textures to detect foreign-matter defect [17].…”

Section: Introductionmentioning

confidence: 99%

MSANet: efficient detection of tire defects in radiographic images

Zhao¹,

Zheng²,

Sun³

et al. 2022

Meas. Sci. Technol.

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Visual inspection has been widely studied and applied in industrial fields. Previous work has investigated the use of established traditional machine learning and deep learning methods to perform automated defect detection for tires. However, intelligent tire defect online detection is still a challenging task due to the complex anisotropic texture background of tire radiographic image. In this paper, we propose an efficient tire defect online detection method named MSANet based on an improved lightweight Yolov4 tiny network. A novel multi-scale self-attention feature enhancement module (MSAM) is designed to extract feature map with rich multi-scale context information. An improved feature pyramid model, named MC-FPN is proposed which utilizes MSAM and convolutional block attention module (CBAM) to enhance the information representation of the feature pyramid. Ablation experiments are conducted to verify the effectiveness of the proposed modules. Comparative experimental results with the state-of-the-art methods validate the effectiveness and efficiency of the proposed method which can achieve a mAP of 96.96% and an average detection time of 30.81ms per image. The proposed method can meet the requirements of industrial online detection by virtue of its lower computational costs, and has good generalization ability in other visual inspection tasks.

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“…Originally, the main focus of the indirect identification technique is to extract the frequencies of the bridge, which is the most basic parameter related to the health status of a bridge. This technique is based on the transformation of the recorded data for the test vehicle from the time domain to the frequency domain using fast Fourier transform (FFT) [ 18 , 19 ], empirical mode decomposition [ 20 ], or other techniques [ 21 , 22 , 23 , 24 ]. Along these lines, Feng and Feng [ 21 ] proposed a bridge damage detection procedure that utilizes the vehicle-induced displacement response of the bridge, particularly, the curvature of the first mode shape, for simulated damage cases.…”

Section: Introductionmentioning

confidence: 99%

“…OBrien and Keenahan [ 22 ] used a vehicle equipped with traffic speed deflectometers (TSDs) for determining the apparent profile of a bridge by an optimization algorithm, and showed that the time-shifted difference in the apparent profile can be probably used as a damage indicator of the bridge in the presence of noise by simulation. Behroozinia and Khaleghian et al [ 23 ] presented a finite element model of the intelligent tire by using implicit dynamic analysis for defect tire detection. McGetrick et al [ 24 ] used the test vehicle to identify the frequency and damping of a bridge, considering both smooth and rough bridge surfaces, and various vehicle speeds.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification of Bridges from Passing Test Vehicles

Yang

Zhu

Wang

et al. 2018

Sensors

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This paper presents two approaches for the structural damage identification of a bridge from the dynamic response recorded from a test vehicle during its passage over the bridge. Using the acceleration response recorded by the vibration sensors mounted on a test vehicle during its passage over the bridge, along with the computed displacement response, the bending stiffness of the bridge can be determined using either: (1) the frequency-domain method based on the improved directed stiffness method with the identified frequency and corresponding mode shape, or (2) the time-domain method based on the residual vector of the least squares method with a fourth-order displacement moment. By comparing the bending stiffness values identified from the vehicle-collected data for the bridge under the undamaged and damaged states that are monitored regularly by the test vehicle, the bridge damage location and severity can be identified. Through numerical simulations and field tests, the present approaches are shown to be effective and feasible.

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Damage diagnosis in intelligent tires using time-domain and frequency-domain analysis

Cited by 19 publications

References 21 publications

Experimental Evaluation of Tire Tread Wear Detection Using Machine Learning in Real-Road Driving Conditions

Experimental Evaluation of Tire Tread Wear Detection Using Machine Learning in Real-Road Driving Conditions

MSANet: efficient detection of tire defects in radiographic images

Structural Damage Identification of Bridges from Passing Test Vehicles

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