Lingfeng Wang¹, Dongbiao Li², Fei Xing^1,3,*, Qiang Wang⁴, Jianjun Shi⁵

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1183-1201, 2025, DOI:10.32604/sdhm.2025.063701 - 05 September 2025

Abstract With the intelligent upgrading of manufacturing equipment, achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity. Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment. However, the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data. A multi-sensor and principal component analysis (PCA) and support vector machine (SVM) asymptotic classification (PCSV) for additive manufacturing fault diagnosis method is proposed, and it divides the fault diagnosis into two steps.… More >

Fangju Zhou¹, Hanbo Zhang², Na Ye¹, Jing Huang¹, Zhu Ren^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4539-4561, 2025, DOI:10.32604/cmc.2025.065946 - 30 July 2025

Abstract This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and a detector. When measurements are transmitted via wireless networks to a remote estimator, the innovation sequence becomes susceptible to interception and manipulation by adversaries. We consider a class of linear deception attacks, wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector. Given the inherent volatility of the detection function based on the detector, we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution… More >

Yang Su^1,*, Xianrang Shi¹, Tinglun Song²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5831-5848, 2025, DOI:10.32604/cmc.2025.064036 - 19 May 2025

Abstract Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving. This study aims to enhance the learning efficiency of multi-sensor feature fusion in autonomous driving tasks, thereby improving the safety and responsiveness of the system. To achieve this goal, we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities: visual, radar, and lidar data. The model optimizes the feature fusion process through the introduction of two novel mechanisms: Sparse Channel Pooling (SCP) and Residual Triplet-Attention… More >

Xinxin Liu, Bin Ye^*

Computer Systems Science and Engineering, Vol.49, pp. 287-300, 2025, DOI:10.32604/csse.2025.061655 - 20 February 2025

Abstract In the broader field of mechanical technology, and particularly in the context of self-driving vehicles, cameras and Light Detection and Ranging (LiDAR) sensors provide complementary modalities that hold significant potential for sensor fusion. However, directly merging multi-sensor data through point projection often results in information loss due to quantization, and managing the differing data formats from multiple sensors remains a persistent challenge. To address these issues, we propose a new fusion method that leverages continuous convolution, point-pooling, and a learned Multilayer Perceptron (MLP) to achieve superior detection performance. Our approach integrates the segmentation mask with… More >

Zhaowei Zhou¹, Kaicheng Ruan¹, Donghua Zhao¹, Yi Xiong^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011376

Abstract Fused deposition modeling (FDM) with foaming materials offers the capability to generate internal porous structures through in-situ foaming, imparting favorable characteristics such as weight reduction, shock absorption, thermal insulation, and sound insulation to printed objects. However, the process planning for this technology presents challenges due to the difficulty in accurately controlling the foaming rate, stemming from a complex underlying mechanism that remains poorly understood. This study introduces a multi-sensor platform for FDM with foaming materials, facilitating in-situ process monitoring of temperature field information during material modeling and quality evaluation of printed objects, i.e., abnormal foaming… More >

Chen Chen^1,2, Zhongwei Xu¹, Meng Mei^1,*, Kai Huang³, Siu Ming Lo²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4533-4549, 2024, DOI:10.32604/cmc.2024.048995 - 20 June 2024

Abstract Railway switch machine is essential for maintaining the safety and punctuality of train operations. A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein. Unlike existing methods, this approach takes into account the spatial information of the time series monitoring data, aligning with the domain expertise of on-site manual monitoring. Besides, a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient information. First, one-dimensional signal data is preprocessed and transformed into two-dimensional images. Afterward, the fusion feature tensor is created by More >

Jiajie Shen¹, Yan Wang^1,*, Dongxu Zhang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.047903 - 20 June 2024

Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions.… More >

Kuosheng Jiang¹, Chengrui Han¹, Yasheng Chang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3329-3352, 2024, DOI:10.32604/cmes.2024.046729 - 11 March 2024

Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used More >

Guangxin Zhang¹, Minghui Liu², Shichao Cheng³, Minzhen Wang^1,*, Changshun Zhao⁴, Hongdan Zhao⁵, Gaiming Zhong¹

Energy Engineering, Vol.121, No.1, pp. 169-185, 2024, DOI:10.32604/ee.2023.027907 - 27 December 2023

Abstract The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission. The tower’s tilt and severe deformation will cause the building to collapse. Many small changes caused the tower’s collapse, but the early staff often could not intuitively notice the changes in the tower’s state. In the current tower online monitoring system, terminal equipment often needs to replace batteries frequently due to premature exhaustion of power. According to the need for real-time measurement of power line tower, this research designed a real-time… More >

Ting Zhao¹, Weiping Ding¹, Haibo Huang¹, Yudong Wu^1,2,*

Sound & Vibration, Vol.57, pp. 133-153, 2023, DOI:10.32604/sv.2023.044203 - 21 December 2023

Abstract The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies. However, some micro-motors may exhibit design deficiencies, component wear, assembly errors, and other imperfections that may arise during the design or manufacturing phases. Consequently, these micro-motors might generate anomalous noises during their operation, consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers. Automobile micro-motors exhibit a diverse array of structural variations, consequently leading to the manifestation of a multitude of distinctive auditory irregularities. To address the identification of diverse forms of abnormal… More > Graphic Abstract