An Inter-Shaft Bearing Fault Diagnosis Dataset from an Aero-Engine System

In this paper,the aero-engine test with inter-shaft bearing fault is carried out,and a dataset is proposed for the first time based on the vibration signal of rotors and casings.First,a test rig based on a real aero-engine is established,driven by motors and equipped with a lubricating system.Then,the aero-engine is disassembled and assembled following the specification process,and the inter-shaft bearing with artificial fault is replaced.Next,the aero-engine test is conducted at 28 groups of high-and low-pressure speeds.Six measuring points are arranged,including two displacement sensors to test the displacement vibration signals of the low-pressure rotor and four acceleration sensors to test the acceleration vibration signals of the casing.The test results are integrated into an inter-shaft bearing fault dataset.Finally,based on the dataset in this paper,frequency spectrum,envelope spectrum,CNN,LSTM,and TST are used for fault diagnosis,and the results are compared with those of CWRU and XJTU datasets.The results show that the characteristic fault frequency cannot be found directly in the spectrum and envelope spectrum corresponding to this paper’s dataset but in CWRU and XJTU datasets.Using CNN,LSTM,and TST for fault diagnosis of the dataset in this paper,the accuracy is 83.13%,85.41%,and 71.07%,respectively,much lower than the diagnosis results of CWRU and XJTU datasets.It can be seen that the dataset in this paper is closer to the actual fault diagnosis situation and is a more challenging dataset.This dataset provides a new benchmark for the validation of fault diagnosis methods.Mendeley data:https://github.com/HouLeiHIT/HIT-dataset.

Special Issue on Machine Fault Diagnostics and Prognostics

Machine components and systems, such as gears, bearings, pipes, cutting tools and turbines, may experience various types of faults, such as breakage, crack, pitting, wear, corrosion. If not being properly monitored and treated, such faults can propagate and lead to machinery perfor- mance degradation, malfunction, or even severe compo- nent/system failure. It is significant to reliably detect machinery defects, evaluate their severity, predict the fault propagation trends, and schedule optimized maintenance and inspection activities to prevent unexpected failures. Advances in these areas will support ensuring equipment and production reliability, safety, quality and productivity.

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Deep learning techniques have outstanding performance in feature extraction and modelfitting.In thefield of aero-engine fault diagnosis,the intro-duction of deep learning technology is of great significance.The aero-engine is the heart of the aircraft,and its stable operation is the primary guarantee of the aircraft.In order to ensure the normal operation of the aircraft,it is necessary to study and diagnose the faults of the aero-engine.Among the many engine fail-ures,the one that occurs more frequently and is more hazardous is the wheeze,which often poses a great threat toflight safety.On the basis of analyzing the mechanism of aero-engine surge,an aero-engine surge fault diagnosis method based on deep learning technology is proposed.In this paper,key sensor data are obtained by analyzing different engine sensor data.An aero-engine surge data-set acquisition algorithm(ASDA)is proposed to sample the fault and normal points to generate the training set,validation set and test set.Based on neural net-work models such as one-dimensional convolutional neural network(1D-CNN),convolutional neural network(RNN),and long-short memory neural network(LSTM),different neural network optimization algorithms are selected to achieve fault diagnosis and classification.The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis.The aero-engine surge fault diagnosis network(ASFDN)proposed in this paper achieves better results.Through training,the network achieves more than 99%classification accuracy for the test set.

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Supervised fault diagnosis typically assumes that all the types of machinery failures are known.However,in practice unknown types of defect,i.e.,novelties,may occur,whose detection is a challenging task.In this paper,a novel fault diagnostic method is developed for both diagnostics and detection of novelties.To this end,a sparse autoencoder-based multi-head Deep Neural Network(DNN)is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data.The detection of novelties is based on the reconstruction error.Moreover,the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function,instead of performing the pre-training and fine-tuning phases required for classical DNNs.The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer.The results show that its performance is satisfactory both in detection of novelties and fault diagnosis,outperforming other state-of-the-art methods.This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect,but also detect unknown types of defects.

Gas Path Fault Tolerant Control of Aero-Engine Based on On-Board Model

A fault tolerant control method is proposed in this paper for a turbofan engine gas path degradation through the full flight envelope. A Quantum-behaved Particle Swarm Optimization(QPSO) algorithm is applied to obtain engine inputs adjustments, which contribute to construct off-line performance accommodation interpolation schedules. With a double closed-loop control system structure, command control is corrected based on real-time fault diagnostic results. Simulations indicate that fault tolerant control could reduce thrust and stall margin loss effectively in gas path faults.

The Implementation of Fault Diagnostic Expert System for Personal Computer

In this paper a PC fault diagnostic expert system (PCDGES) is introduced, which can be run under CCDOS and encoded by English Prolog and C. In the system, a method of combining logic with production rules is applied to represent knowledge. The expert system program is separated from knowledge base. Inference computation is mainly carried backward, and the forward is regarded as an auxiliary inference. The knowledge base can be easily updated, deleted and added in operation time. It has a supporting machanism for the acquisition of knowledge and by means of “telling method”, knowledge can be acquisited. The system also has “why” explanation function and an interface with DOS, full screen editor, and hardware dignostic program. For Chinese users, all the prompt information and selection menus are displayed in color Chinese.

Intelligent System for Parallel Fault-Tolerant Diagnostic Tests Construction

This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.

面向残差评价的中点钳位型逆变器开路故障诊断方法

在基于解析模型的故障诊断系统中,为了显化残差信号所包含故障信息的能力,提出一种面向残差评价的中点钳位型三电平三相逆变器快速开路故障诊断方法。首先,构建诊断观测器获取三相电流观测值并通过观测矩阵计算电流残差;其次,通过引入基于范数的标准评价函数,计算残差性能指标与阈值,将残差转换为可用的特征信号并与阈值相比较实现高效实时的故障检测;再次,推导不同功率开关器件开路故障状态下的电流残差特性,并依据此特性实现精准的故障隔离;最后,在仿真平台及半实物实验平台上验证了该文所提算法的有效性与可行性。仿真与实验平台结果表明,相较于传统基于信号与知识的诊断方法,残差性能评价的引入使得诊断速度提高80%以上。针对NPC型三电平三相逆变器常见的四种开路故障情况,均可实现快速且精准的故障检测及定位。

智能化技术在新能源汽车故障诊断中的应用

阐述新能源汽车故障诊断的特点,探讨智能化电子诊断技术的应用,包括故障预测、故障定位、故障诊断和故障修复。分析新能源汽车故障诊断中的优势和不足,展望发展趋势。

一例保护误动引起110kV主变开关跳闸的故障分析

本文对某盐化工企业发生的一例因微机保护装置误动作,引起整流110kV主变压器开关跳闸的故障进行分析,及时处理,并提出了防范对策,从中吸取了深刻教训,做到了防范于未然。

EHDE和WHO-SVM模型在齿轮箱故障诊断中的应用

针对现有齿轮箱故障诊断方法对数据长度敏感的缺陷,提出了一种基于增强层次多样性熵(EHDE)和野马算法(WHO)优化支持向量机(SVM)的齿轮箱故障诊断模型。首先,传统熵值特征提取方法在特征提取阶段对数据样本的长度比较敏感,为此提出了增强层次多样性熵,并将其作为特征提取指标用于提取齿轮箱的故障特征;其次,采用WHO算法对SVM模型的参数进行了优化,建立了参数最优的WHO-SVM分类器;最后,将故障特征样本输入至WHO-SVM分类器中进行了训练和识别,完成了样本的故障识别;利用齿轮箱数据集分别从数据长度敏感性、算法特征提取时间、模型诊断性能三种角度对EHDE、精细复合多尺度样本熵、精细复合多尺度模糊熵、精细复合多尺度排列熵、精细复合多尺度散布熵、精细复合多尺度波动散布熵进行了对比研究。研究结果表明:EHDE方法对数据长度的要求较低,在数据长度为512时即可以取得99.1%的平均识别准确率,在诊断稳定性和诊断精度方面均优于其他对比方法;在算法的泛化性实验中,EHDE方法能够以98%的准确率识别齿轮箱的不同故障类型,具有明显的泛化性和通用性。

空时模型融合自注意力机制的航空管路故障诊断新方法

航空发动机外部液压管路结构复杂且管路故障信号中伴随着强大的噪声干扰,导致航空管路故障特征提取十分困难,同时目前诊断模型参数和计算量比较庞大,不适合高效地移植到移动和嵌入式设备。面对上述挑战,提出一种轻量化空时模型融合注意力机制的航空发动机液压管路故障诊断新方法,并命名为S-Bi-ATM-Net模型,首先,设计轻量化管路空间特征提取模块,从而融合管路故障数据的空间特征,抽取数据的细粒度特征;然后,设计轻量化管路时间特征提取模块,从细粒度特征中继续融合管路粗粒度特征,实现粗细粒度特征融合;此外在空时模型中融合自注意力机制进行优化,使得最后的决策更加的聚焦,进一步提高所提模型的诊断精度。基于同一数据集,将所提方法与目前主流方法对比分析发现,所提方法能更准确地识别航空管路不同的故障状态,证明了所提方法的优越性和稳定性。

应用阻抗谱的锂电池模组早期内短路故障诊断

针对锂电池模组内部内短路故障单体诊断困难、检测效率低下的问题,提出应用电荷传递内阻和固体电解质界面膜内阻比值变化幅度的锂电池模组早期内短路故障诊断方法。所提方法基于电化学阻抗谱对内短路故障演化过程中锂电池内部电化学反应变化进行监测,利用弛豫时间分布方法针对阻抗谱特征进行量化分析。在阻抗谱由单体维度到模组维度正向推衍的基础上,筛选满荷电状态下电荷传递内阻和固体电解质界面膜内阻作为主要的评估指标,并根据阈值诊断模型搭建实验平台进行验证。实验结果表明:欧姆内阻和电荷传递内阻随着内短路故障程度的加深逐渐增大,固体电解质界面膜内阻整体呈现出逐渐减小的趋势;当模组电池数控制在8节以下时,通过阈值诊断模型对存在故障单体的模组识别准确率可以达到100%。通过对阻抗谱特征的精确辨识与分析,该方法能够有效实现锂电池模组早期内短路故障的准确诊断。

光伏阵列故障诊断技术综述

首先,该文梳理光伏阵列中不同类型的故障,将其分为组件故障和组件间故障两类,并阐述这些故障类型的特点和成因。其次,基于光伏组件内的单二极管等效电路模型,分析光伏组件在不同辐照度和温度条件下的电压-电流特性变化,并根据故障下电压-电流特性变化情况,将光伏组件故障分为非失配型故障和失配型故障。然后,着重论述现有的光伏阵列故障诊断领域重要技术和前沿研究,按照故障类型特点和检测手段的差异,将诊断技术方法归纳为视觉与成像诊断方法和电气特征参数诊断方法两大类。最后,指出光伏故障诊断技术的不足与发展趋势,为光伏系统智能化运维提供了参考。

Philips Digital Diagnost VS故障维修

0引言飞利浦公司的Digital Diagnost VS系统取消了暗盒技术,采用目前世界上最先进的探测器,其高速的光量子转换效率、开放式的网络结构设计,提高了工作效率和检查患者的速度,实现了患者胸部、骨骼等全身多部位的数字化检查。现将该设备使用期间的一些故障现象与分析排除方法介绍如下。

适用于有源区域电磁波时程精细积分仿真的大时间步长矩阵数值积分方法

时程精细积分(PITD)法是一种全波电磁数值方法,具有相当宽松的数值稳定性条件,且数值色散误差几乎不受时间步长的影响,因而近年来在电磁学领域中受到广泛关注。在有源区域的电磁数值模拟中,PITD法需要计算激励源添加引入的非齐次项所产生的矩阵积分,其计算或存在矩阵求逆运算,易不稳定,或难以在大时间步长下保证计算结果的精度,这使得PITD法的推广应用受到一定限制。该文将响应矩阵法引入有源区域的电磁波PITD仿真中,不仅避免了矩阵的求逆运算,并且能够在保证计算结果精度的条件下,增大时间步长的选择范围,提高算法的计算效率。最后,进行了实例计算,并与直接近似积分方法和数值积分方法的结果进行了对比,验证了该文所提方法的实用性和有效性。

基于PSO的公共建筑电气设备绝缘故障诊断系统设计

公共建筑电气设备在能源转换时,设备工作时间长会使设备线路出现绝缘故障,导致设备无法使用。为此,设计了基于PSO的公共建筑电气设备绝缘故障诊断系统。构建公共建筑电气设备绝缘故障诊断系统框架,分析系统各硬件模块功能,完成系统硬件设计。根据B/S结构,确定系统软件结构,结合PSO算法和神经网络,实现公共建筑电气设备绝缘故障诊断。实验结果表明,该系统的平均故障诊断时间仅为7.29 ms,故障位置振动幅值和电压电流变化情况与实际一致,具有较好的故障诊断效果,能够有效提高故障诊断精度和效率。

基于故障树分析的电缆故障诊断可靠性评估方法研究

提出了一种基于故障树分析的电缆故障诊断方法。介绍了故障树分析技术的基本概念和方法。深入探讨了电缆故障的类型和成因。基于故障树构建了电缆故障诊断模型,并通过案例分析进行了验证。最后,对电缆故障的可靠性进行了评估。为电缆系统的可靠性评估提供了一种新的方法,对电缆故障的预防和处理具有重要意义。

基于深度残差网络的电机故障诊断研究

电机故障诊断技术的研究对于保障安全生产、减少机械故障、减轻生产损失有重要的现实意义。针对传统机器学习的故障诊断方法的局限性,提出基于深度残差网络(ResNet)的电机故障诊断方法。首先,分析了传统的电流信号特征分析方法。然后,建立了深度ResNet故障诊断框架。最后,通过设计不同模式的三相电流输入策略,建立特征自适应提取的深度学习电机故障诊断模型,有效提取了电机电流信号的故障深度特征,并通过对比试验验证了诊断效果。试验结果表明,该方法准确率高于传统机器学习方法。该研究为深度ResNet在电机故障诊断领域的推广应用奠定基础。

基于载波调制的光纤通信故障数据诊断方法

光纤通信信号一般具有复杂的低频成分,在通信信道中传输难以匹配信道特性,导致光纤通信故障诊断准确度不高,因此提出了一种基于载波调制的光纤通信故障数据诊断方法。该方法通过采集并预处理光纤通信信号,将采集信号加载到高频载波上实现载波调制,然后输入BP神经网络进行初步故障诊断。进一步利用换班测试法确定故障数据在通信链路上的位置。实验结果表明,该方法在实现光纤通信故障数据诊断的同时,能够准确定位故障数据在通信链路中的位置。