Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor

Xue, Hongtao; Song, Ziwei; Wu, Mian; Sun, Ning; Wang, Huaqing

doi:10.3390/s22166316

Cited by 5 publications

(4 citation statements)

References 48 publications

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“…The communication method follows the standard IIC communication protocol at a frequency of 400 kHz (Max). The hardware configuration of the entire cutting system described above shows that the laptop is powered by 220 V AC, the DC motor and stepper motor are powered by 36 V, and the microcontroller is powered by 5 V. This cutting system is equipped with a 48 V lithium battery, which obtains a 5 V power supply from the lithium battery through a step-down module to power the microcontroller [ 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode

Wang

Xiong

et al. 2022

Sensors

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China is the world’s third-largest producer of sugarcane, slightly behind Brazil and India. As an important cash crop in China, sugarcane has always been the main source of sugar, the basic strategic material. The planting method of sugarcane used in China is mainly the pre-cutting planting mode. However, there are many problems with this technology, which has a great impact on the planting quality of sugarcane. Aiming at a series of problems, such as low cutting efficiency and poor quality in the pre-cutting planting mode of sugarcane, a sugarcane-seed-cutting device was proposed, and a sugarcane-seed-cutting system based on automatic identification technology was designed. The system consists of a sugarcane-cutting platform, a seed-cutting device, a visual inspection system, and a control system. Among them, the visual inspection system adopts the YOLO V5 network model to identify and detect the eustipes of sugarcane, and the seed-cutting device is composed of a self-tensioning conveying mechanism, a reciprocating crank slider transmission mechanism, and a high-speed rotary cutting mechanism so that the cutting device can complete the cutting of sugarcane seeds of different diameters. The test shows that the recognition rate of sugarcane seed cutting is no less than 94.3%, the accuracy rate is between 94.3% and 100%, and the average accuracy is 98.2%. The bud injury rate is no higher than 3.8%, while the average cutting time of a single seed is about 0.7 s, which proves that the cutting system has a high cutting rate, recognition rate, and low injury rate. The findings of this paper have important application values for promoting the development of sugarcane pre-cutting planting mode and sugarcane planting technology.

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Section: Methodsmentioning

confidence: 99%

Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode

Wang

Xiong

et al. 2022

Sensors

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“…Vibration signals contain an abundance of information, and vibration signals are often used to monitor the running state and diagnose a fault in the field of fault diagnosis [44,45]. For a mechanical fault of an in-wheel motor, common bearing faults are regarded as a representative of measured vibration signals.…”

Section: Introductionmentioning

confidence: 99%

In-Wheel Motor Fault Diagnosis Using Affinity Propagation Minimum-Distance Discriminant Projection and Weibull-Kernel-Function-Based SVDD

Liu

Xue

Ding

et al. 2023

Sensors

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To effectively ensure the operational safety of an electric vehicle with in-wheel motor drive, a novel diagnosis method is proposed to monitor each in-wheel motor fault, the creativity of which lies in two aspects. One aspect is that affinity propagation (AP) is introduced into a minimum-distance discriminant projection (MDP) algorithm to propose a new dimension reduction algorithm, which is defined as APMDP. APMDP not only gathers the intra-class and inter-class information of high-dimensional data but also obtains information on the spatial structure. Another aspect is that multi-class support vector data description (SVDD) is improved using the Weibull kernel function, and its classification judgment rule is modified into a minimum distance from the intra-class cluster center. Finally, in-wheel motors with typical bearing faults are customized to collect vibration signals under four operating conditions, respectively, to verify the effectiveness of the proposed method. The results show that the APMDP’s performance is better than traditional dimension reduction methods, and the divisibility is improved by at least 8.35% over the LDA, MDP, and LPP. A multi-class SVDD classifier based on the Weibull kernel function has high classification accuracy and strong robustness, and the classification accuracies of the in-wheel motor faults in each condition are over 95%, which is higher than the polynomial and Gaussian kernel function.

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“…Comparative analysis was conducted against other methods, including support vector machines (SVMs), a particle swarm optimization-based SVM (PSO-SVM), among others. The doubleoptimized AHN method exhibited superior performance, achieving a diagnosis accuracy surpassing 80% [18].…”

mentioning

confidence: 99%

“…These instances represent just a few examples of the diverse applications where AHN has demonstrated favorable outcomes. For readers seeking more in-depth information on specific cases mentioned here or desiring a broader understanding of the varied purposes for which AHN has been employed, it is recommended to explore the lectures by Ponce et al referenced in this work [13][14][15][16][17][18][19][20][21][22].…”

mentioning

confidence: 99%

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

González-Núñez,

Trejo,

Kampouridis

2024

BDCC

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This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7×10−4. Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting.

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Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor

Cited by 5 publications

References 48 publications

Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode

Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode

In-Wheel Motor Fault Diagnosis Using Affinity Propagation Minimum-Distance Discriminant Projection and Weibull-Kernel-Function-Based SVDD

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

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