Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.

Exploration of Teaching Reform of Food Machinery and Equipment Course Based on New Engineering Concept

The new engineering concept aims to train high-quality engineering talents to meet the needs of future science and technology and industrial development through the reform of education and teaching.Under the background of"new engineering",by introducing cutting-edge knowledge of the industry and interdisciplinary integration,adopting innovative teaching methods such as project-driven teaching and flipped classroom,strengthening experimental teaching and school-enterprise cooperation,and establishing comprehensive evaluation and feedback mechanism,Food Machinery and Equipment course is reformed to improve the teaching quality and train high-quality engineering talents to meet the needs of modern food processing industry.

Italian textile machinery orders remain stable in Q1 2024

For Italian textile machinery sector,2024 has begun without anything seemingly special.The first quarter has seen the orders index,as reported by the Economics Department of ACIMIT-the Association of Italian Textile Machinery Manufacturers-remain stationary compared to the same period the previous year.In absolute terms,the index came in at 61.2 points(basis:2021=100).

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.

Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions

Variational mode decomposition(VMD)is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters:the decomposed number K and penalty factorαunder strong noise interference.To solve this issue,this study proposed self-tuning VMD(SVMD)for cavitation diagnostics in fluid machinery,with a special focus on low signal-to-noise ratio conditions.A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition.A hybrid optimized sparrow search algorithm(HOSSA)was developed for optimalαfine-tuning in a refined space based on fault-type-guided objective functions.Based on the submodes obtained using exclusive penalty factors in each iteration,the cavitation-related characteristic frequencies(CCFs)were extracted for diagnostics.The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition.The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs.Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost.SVMD especially enhances the denoising capability of the VMD-based method.

Compound Fault Diagnosis for Rotating Machinery:State-of-the-Art,Challenges,and Opportunities

Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

A Multi-mode Electronic Load Sensing Control Scheme with Power Limitation and Pressure Cut-off for Mobile Machinery

In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.

Using Digital Twin to Diagnose Faults in Braiding Machinery Based on IoT

The digital twin(DT)includes real-time data analytics based on the actual product or manufacturing processing parameters.Data from digital twins can predict asset maintenance requirements ahead of time.This saves money by decreasing operating expenses and asset downtime,which improves company efficiency.In this paper,a digital twin in braiding machinery based on IoT(DTBM-IoT)used to diagnose faults.When an imbalance fault occurs,the system gathers experimental data.After that,the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for defect detection.It is possible to anticipate asset maintenance requirements with DT technology by IoT(Internet of Things)sensors,XR(X-Ray)capabilities,and AI-powered analytics.A DT model’s appropriate design and flexibility remain difficult because of the nonlinear dynamics and unpre-dictability inherent in the degrading process of equipment.The results indicate that the DT in braiding machinery developed allows for precise diagnostic and dynamic deterioration analysis.At least there is 37%growth in efficiency over conventional approaches.

Nonfarm employment, agricultural machinery service purchase and agricultural production efficiency:An empirical study based on China's major wheat-producing areas

Based on the cross-sectional data of the survey conducted in China's main wheat producing areas at the end of 2019,this paper uses the translog production function model to estimate agricultural technical efficiency,and studies the relationship between nonfarm employment,agricultural machinery service purchase and agricultural production efficiency.The results show that the household non-agricultural employment ratio and non-agricultural income have a significant positive impact on the purchase of agricultural machinery services.In addition to the effect of scale efficiency,non-agricultural employment does not have a significant impact on agricultural technical efficiency,and labor transfer does not have a significant negative impact on agricultural production.

中国大田无人农场关键技术研究与建设实践

智慧农业是现代农业的发展方向,无人农场是实现智慧农业的重要途径。为了探索和推广无人农场在现代农业中的应用,华南农业大学对大田无人农场的关键技术进行了深入研究,包括无人农场作业环境、作业对象和作业机械装备信息的数字化感知技术;土地整治、耕整、种植、播种、田间管理和收获方案的智能化决策技术;农机自动导航和农机精准作业的精准化作业技术;农作物生长、农机运维和农场经营管理的智慧化管理技术。2020年在广东增城创建全球首个无人农场,实现了五大功能,包括耕种管收生产环节全覆盖,机库田间转移作业全自动,自动避障异况停车保安全,作物生产过程实时全监控,智能决策精准作业全无人。取得了显著的经济、社会和生态效益,2021年广东增城水稻无人农场种植的优质丝苗米十九香产量达到9934.35 kg/hm^(2),比当地的平均产量高32%;2023年湖南益阳千山红镇再生稻无人农场两季产量达到17988 kg/hm^(2),说明了人不下田也能种地,也能种好地。截至2023年11月,在国内15个省启动了30个无人农场的建设,包括水稻、小麦、玉米和花生4种作物,实践结果证明了无人农场和智慧农业发展的巨大潜力,为解决“谁来种地”和“如何种地”提供了重要途径。

Jingwei textile machinery:A variety of core equipment appeared at the exhibition

Intellectualization and sustainable development are still the mainstream trends of textile industry.ITMA 2023,a four-year textile machinery event,arrived as scheduled and to be held in Milan,Italy from June 8 to 14.This grand meeting will certainly give a strong impetus to"Transfroming the World of Textiles".

离心式高速玉米精量排种器T形槽型孔设计与试验

针对离心式高速玉米精量排种器双粒种子并排填充导致重播严重的问题,该研究提出利用型孔槽对称凸台结构降低玉米种子并排填充概率的方法,设计了一种T形槽型孔。通过构建充种阶段玉米种子的力学模型,并结合玉米种子的形状特征确定了T形槽型孔基本结构参数。借助EDEM离散元仿真软件,以型孔槽前端长度、型孔槽后端面倾斜角以及型孔槽底部倾斜角为因素,以合格指数、重播指数与漏播指数为评价指标,设计二次正交旋转回归组合仿真试验。仿真试验结果表明:在作业速度18 km/h条件下,型孔槽前端长度为9.31 mm,型孔槽后端面倾斜角为43.37°与型孔槽底部倾斜角为70.5°时,排种质量最优,排种合格指数、重播指数与漏播指数分别为94.03%、1.72%与4.25%。台架验证试验结果表明:作业速度为15 km/h时,T形槽型孔的合格指数最高为95.16%,在此作业速度下,排种器的重播指数为1.42%,漏播指数为3.42%,满足高速精量播种要求;作业速度在12~21 km/h内时,T形槽型孔的平均合格指数为94.77%,相较于矩形槽型孔提升了3.13个百分点,相较于蹄型槽型孔提升了1.80个百分点,T形槽型孔的平均重播指数为2.06%,相较于矩形槽型孔降低了3.41个百分点,相较于蹄型槽型孔降低了2.49个百分点。该研究可为离心式高速玉米精量排种器优化提供参考。

ACIMIT forecasts show an increase in global demand for textile machinery from mid-2023--Interview with Mr.Alex Zucchi,President of ACIMIT,Association of Italian Textile Machinery Manufacturers

For the past year, according to preliminary data in 2022, the value of Italian machinery production is expected to exceed 2.6 billion euro, up about 11% on the previous year. Exports account for more than 87% of this value. Despite the negative factors(inflationary pressures and political crises caused by the pandemic and Russian-Ukrainian war) even in 2022 the upward trend of our industry goes on.

不同机械压实条件下黑土性质及入渗特征变化研究

为探究不同机械压实条件对黑土土壤性质入渗特征的影响,以克山县典型黑土为研究对象,设置3种不同型号机械(华夏HX2104-C:154 kW、约翰迪尔554:40 kW、中国海山:19 kW)和不同压实次数(0,1,3,5,7,9次)对秋收后大豆耕地垄沟开展模拟压实,通过测定土壤容重、含水率、孔隙及土壤入渗特征,分析不同机械压实条件对土壤物理性质及土壤入渗特征的影响。结果表明:在0~10 cm土层,大型、中型、小型机械压实后土壤容重分别增加54.17%、56.25%、46.88%;总孔隙度分别减小31.58%、28.07%、29.82%;含水率分别下降19.35%、16.13%、9.68%。首次压实对土壤性质的影响最显著,容重均增加32.29%以上,土壤总孔隙度均减小15.8%以上,只有大型机械使土壤含水率显著减小9.6%。随压实次数增加,各机械压实后10~20 cm土层土壤容重从1.25 g·cm^(-3)增加至1.58 g·cm^(-3),总孔隙度从52.16%减小至38.22%,土壤含水率从27.33%下降至20.67%,其中,大型机械在首次压实使容重及孔隙特征产生显著影响,而中型及小型机械对土壤物理性质的影响主要依靠压实次数的增加对土壤起到压实的累积作用。首次压实,中型及小型机械使2 h累计入渗量分别减少87.16%、50.81%,初渗速率分别减少87.59%、70.93%,中型机械使稳渗速率显著减少92.90%;3次压实,大型机械使2 h累计入渗量、初渗速率分别显著减小73.29%、75.67%,大型及小型机械使稳渗速率分别显著减少74.07%、66.97%;至5次压实后土壤各入渗特征无显著差异。0~20 cm土层土壤容重及土壤孔隙特征均影响机械压实土壤入渗特征,其中,0~10 cm土层土壤容重是影响综合土壤入渗特征的主导因素。

草莓移栽机控制系统设计与试验

针对草莓高起垄定向移栽农艺要求,设计一种机械打穴、人工识别草莓苗弓背方向后定向穴栽的移栽机。基于可编程控制器(PLC)设计整机控制系统,包括移栽机定速行走控制系统、前轮转向控制系统、打穴控制系统等任务模块。整机采用直流无刷电机作为底盘的驱动元件,根据电机自带编码器的输出值进行行走速度、栽植株距和打穴频率的协调控制,采用步进电机作为打穴机构的驱动元件,由接近开关对打穴机构进行定位,通过二者配合完成打穴过程,在人机协同作业下,完成草莓苗移栽。试验结果表明:当单行移栽效率为15株/min,株距为150 mm、200 mm、250 mm时,株距合格率、孔深合格率、孔径合格率均在95%以上,其变异系数均小于5%,打穴成功率均大于95%,符合移栽机行业标准,打穴移栽效果满足农艺要求。

荸荠收获机弹簧辊式泥果分离装置研制

针对荸荠收获泥果分离难、果实损伤率高的问题,根据旱地环境下荸荠采收作业需求,该研究提出一种由正反旋弹簧并排布置的荸荠收获机弹簧辊式泥果分离装置。通过对荸荠与弹簧辊的相对运动过程动力学分析,确定了影响荸荠收获泥果分离的关键因素为弹簧外径、螺距、相邻弹簧间距、高度差及弹簧转速、线径、作业速度。利用EDEM软件建立泥果混合物离散元模型,对弹簧辊结构参数与工作参数进行单因素试验,分析各因素对泥果分离效果的影响。以荸荠筛分率和土壤筛分率为指标进行二次回归正交试验,得到弹簧辊最佳参数组合为外径100 mm、螺距30 mm、间距9 mm、转速420 r/min,该参数组合下荸荠筛分率为80.00%,土壤筛分率为80.69%。进行仿真验证试验,对比试验结果与模型预测值,荸荠筛分率平均相对误差为2.09%,土壤筛分率平均相对误差为2.42%。以明果率、伤果率、破皮率、挖净率为指标开展模拟采挖试验,分析不同线径弹簧辊的泥果分离能力,确定12 mm线径弹簧兼具较好的振动筛分性能和较低的损伤率。通过实际收获试验测得作业速度0.21 m/s,作业效率0.19 m^(2)/s,碎土率75.61%,明果率82.42%,伤果率14.73%,破皮率7.01%。研究结果可为荸荠收获机研制和优化改进提供参考。

“三教”改革背景下高职“汽车机械基础”教学改革研究

【目的】提高“汽车机械基础”课程教学效果,培养学生的工程实践能力和职业素养。【方法】针对“汽车机械基础”课程存在涵盖知识面广,知识点零散;课程教学目标与企业对接不够深入,学生对新技术、新方法、新思路掌握不足;教学过程中的学生兴趣不是很高,课堂参与率低,学生解决问题的能力欠缺等问题,课题组开展“三教”改革背景下高职“汽车机械基础”课程教学改革。课程教学改革可以从三方面着手,即以学生为中心,以学习成果为导向,从教师、教材、教法三方面,依靠“学习通”平台,精准完成学情分析;改革教学模式、教学内容、教学方法、教学评价;开展第一课堂教学实施。【结果】通过改革,学生主体地位得到贯彻,学生积极性不断提高,课堂参与度将持续上升;学生的创造性得到尊重,能够提高机械设计能力、创新设计能力;学生的主体性得到强化,团队协作精神、工匠精神等获得了训练和提高。【结论】高职“汽车机械基础”教学改革模式对其他课程改革,有着十分可行的可复制性和广泛的应用前景。

农业机械电气自动化系统的远程监控与控制技术研究

农业安全问题是农业生产中的关键问题,农业机械电气自动化系统可以减少农业机械设备的故障频发,增加农业机械的使用寿命,在农业生产中至关重要。基于总线技术、无线通信技术的农业机械远程监控系统是智能化的管理系统,由监控中心、通信网络、农业机械系统终端组成。将监控设备与监控中心建立连接,中心服务器通过移动通信技术获取农业机械传感器信息,并储存在服务器数据库中,通过监控设备实现对农业机械的远程监控。提高了农业生产的安全性和农业机械的工作效率。

机耕道自动驾驶农机局部路径规划

针对机耕道场景下自动驾驶农机行驶的安全性、平稳性与规划实时性的实际需求,该研究提出了一种基于二次规划的局部路径规划方法。首先基于有限状态机构建农机机耕道行驶模式,其次采用横纵向解耦的方法,通过改进状态栅格法分别对农机速度行为和轨迹行为进行决策,随后利用二次规划方法生成满足多目标、多约束条件的农机轨迹和速度,得到最优路径,最后在多种行驶环境中进行仿真和实车试验,行驶参考速度为2 m/s。实车试验结果表明,在绕行静态障碍物场景中,规划轨迹的平均绝对曲率为0.021 m^(-1),最大绝对曲率为0.056 m^(-1),平均绝对横向误差为3.23 cm,最大绝对横向误差为8.69 cm,农机与障碍物外轮廓的距离大于0.76 m;在规避相向行驶、同向行驶和横穿机耕道的动态障碍物场景中,规划速度的平均绝对速度误差为0.08~0.12 m/s,绝对速度误差小于0.38 m/s,加速度变化范围为-0.38~0.44 m/s^(2)。在规划周期为200 ms的仿真试验中,该文算法平均耗时48 ms,最大耗时75 ms,相比采用静态状态栅格法平均耗时减少38 ms,算法效率提升44%。研究结果可为机耕道场景下的农机局部路径规划提供技术支持。

基于电动方向盘的拖拉机自动导航转向控制方法

为提高轮式拖拉机自动导航过程中转向控制的精度与稳定性,该研究以雷沃欧豹M704-2H拖拉机作为试验平台,采用电动方向盘作为转向执行机构,分析转向机械间隙对控制精度的影响,针对转向间隙特性设计转向控制算法。首先,为了获得准确的转向角,利用GNSS(global navigation satellite system)与二轮车模型快速标定虚拟轮转角,标定结果表明:虚拟轮转角的最大误差为1.3°,平均误差为0.11°。然后,对转向系统的机械间隙进行分析,设计一种带有间隙补偿的模糊PD(proportional derivative)转向控制算法,并在Simulink中验证算法的可行性。实车试验结果表明,该算法跟踪方波转角信号的响应时间为1.1 s,最大稳态误差为0.65°,平均稳态绝对误差为0.132°。跟踪正弦波转角信号的平均延时为0.5 s,最大误差为1.91°,平均绝对误差为1.09°。与无间隙补偿算法相比,有间隙补偿算法跟踪方波信号最大稳态误差减小了0.022°,平均稳态绝对误差减少了0.112°,角度误差在±0.2°内的时间提升了71%;跟踪正弦波信号最大误差减小了0.68°,平均绝对误差减小0.23°。田间直线导航转向控制试验结果表明,转角跟踪的绝对平均误差为0.61°,最大跟踪误差为2.82°,转向控制跟踪精度较高,稳定性好,满足导航作业需求。