Instantaneous energy density as a feature for gear fault detection

In this work, energy-based features for gear fault diagnosis and prediction are proposed. The instantaneous energy density is shown to obtain high values when defected teeth are engaged. Three methods are compared in terms of sensitivity, reliability and computation effectiveness. The Wigner–Ville distribution is contrasted to the wavelet transform and the newly proposed empirical mode decomposition scheme. It is shown that all three methods are capable of a reliable prediction. An empirical law, which relates the energy content to the crack magnitude is established.     

Input fault detection and estimation using PI observer based on the ARX-Laguerre model

This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2 ^{n d} order electrical linear system.     

高斯混合模型与小波包能量相结合的齿轮故障分类

提出小波包能量与高斯混合模型相结合的齿轮故障分类算法。利用小波包分析提取某种模式下齿轮振动信号多层分解后的不同频带内的能量,并进行归一化处理。然后以各频带能量为元素构造该模式的特征向量,利用这些特征向量以及高斯混合模型良好的数据分布刻画能力,对该模式进行描述。最后采用贝叶斯分类器进行齿轮故障分类。采用该方法对齿轮振动信号进行故障识别,结果表明能取得比人工神经网络算法更高的识别率。     

Enhancement of bearing fault detection using an alternative analytic energy operator and sparse Bayesian step-filtering

Journal of Mechanical Science and Technology - The bearing fault signal is a kind of weak signal, so it is easy to be submerged by background noise. As such, signature extraction is facing a great...     

An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection

Bearings are among the most frequently used components. Bearing failure could lead to complete stall of a mechanical system, unpredicted productivity loss for production facilities or catastrophic consequence for mission-critical equipment. As such, bearing fault detection and diagnosis is an imperative part of most of preventive maintenance procedures. This paper presents a parameter independent yet simple to implement fault detection technique. The Teager energy operator is tailored to extract both the amplitude and frequency modulations of the vibration signals measured from mechanical systems. The incorporation of the frequency modulation information into the proposed bearing fault detection method has eliminated the need for interference removal steps. As the amplitude demodulation (AD) is also inherent in the energy operator, the fault frequency can be detected from the spectrum of the energy-transformed signal. The effectiveness of the proposed method has been validated using both simulated and experimental data.     

基于最优阶次HMM的电机故障诊断方法研究

介绍了一种基于隐马尔可夫模型的电机匝间短路故障诊断方法,方法中提出了一种基于信息熵的隐马尔可夫模型阶次选取策略,该策略根据求解符号出现概率的熵速率与隐马尔可夫模型阶次的关系确定模型最佳阶次,确保在得到合理的模型结构的前提下,使该模型的计算代价最小,从而得到最优隐马尔可夫模型.将该最优隐马尔可夫模型应用于电机匝间短路故障诊断实验中,获得满意的诊断结果,结果表明在保证匝间短路故障诊断精度不变的情况下,通过合理选取隐马尔可夫模型的阶次可以有效地减少模型的计算代价,提高模型的计算效率.     

基于模型压缩的 ED-YOLO 电力巡检 无人机避障目标检测算法

针对现有卷积神经网络模型体积大、运算量高,导致电力巡检无人机检测速率与精度无法兼顾的问题,提出一种基于模型压缩的ED-YOLO网络实现无人机避障的目标检测算法。该目标检测算法以YOLOv4为基础,首先在主干网络中加入通道注意力机制,在不增加计算量前提下提高检测精度;其次在特征金字塔部分运用深度可分离卷积替换传统卷积,减少卷积计算量;最后利用模型压缩策略裁剪网络中冗余通道,减小模型体积并提高模型检测速度。在自主构建的9 600张电力巡检无人机飞行障碍的数据集进行测试,ED-YOLO与YOLOv4相比,其障碍物目标检测的平均精度均值只降低了1.4%,而模型体积减少了94.9%,浮点运算量减少了82.1%,预测速度提升了2.3倍。实验结果表明,对比多种其他现存方法,本文提出的基于模型压缩的ED-YOLO目标检测算法有着精度高、体积小和检测速度快的优势,满足电力巡检无机避障检测要求。     

Investigation of a nonlinear dynamic hydraulic system model through the energy analysis approach

The dynamics of a pressure regulator valve have been studied using the through Bondgraph simulation technique. This valve consists of several elements that can transmit, transform, store, and consume hydraulic energy. The governing equations of the system have been derived from the dynamic model. In solving system equations numerically, various pressure-flow characteristics across the regulator ports and orifices have been taken into consideration. This simulation study identifies some critical parameters that have significant effects on the transient response of the system. The results have been obtained using the MATLAB-SIMULINK environment. The main advantage of the proposed methodology is its ability to model the nonlinear behavior of the hydraulic resistance of system elements as a function of the fluid flow rate.     

基于DPCA方法的故障检测与诊断分析

主元分析(Prlnclpal Component Analysis，PCA)已广泛应用于复杂工业过程的运行状态监控。然而，传统的PCA方法仅构造了生产过程的静态线性关系，无法从根本上有效处理具有较强动态特性的实际工业生产过程。动态主元分析(Dynamic PCA，DPCA)是一种将传统PCA分析推广到动态多变量过程的方法，但其较大的计算负荷阻碍了其实际应用。本文对文献中的DPCA作了算法上的简化，减少了实施中的计算量，并将其应用于重油分馏塔的动态运行故障监测与诊断。研究结果表明了方法的有效性。     

汽油机“失火”原因及检测方法探析

分析了发动机"失火"的主要原因,介绍了常见的检测方法,为发动机"失火"的检测和故障维修,提供了一定的依据。     

Development of the reference model for a residential heat pump system for cooling mode fault detection and diagnosis

Development of a reference model to predict the value of system parameters during fault-free operation is a basic step for fault detection and diagnosis (FDD). In order to develop an accurate and effective reference model of a heat pump system, experimental data that cover a wide range of operating conditions are required. In this study, laboratory data were collected under various operating conditions and then filtered through a moving window steady-state detector. Over five thousand scans of steady-state data were used to develop polynomial regression models of seven system features. A reference model was also developed using an artificial neural network (ANN), and it is compared to the polynomial models.     

Tracy-Widom distribution based fault detection approach: application to aircraft sensor/actuator fault detection

The fault detection approach based on the Tracy-Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy-Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined.     

A real-time data compression algorithm for gear fault signals

A flexible, stable and controllable real-time algorithm of Auto-Regressive and Moving Average based on Swing Door Trending (ARMA-SDT) is proposed for the compression of impact-type signals in gear fault detection systems. The Auto-Regressive and Moving Average (ARMA) model is used to predict the variation trend of signal features. To guarantee the adaptability, an empirical equation is proposed to calculate the compression threshold of the Swing Door Trending (SDT). Based on the empirical equation and prediction results, dynamic self-regulation of compression threshold is realized, and the compression error always stays around a preconfigured value. Moreover, an experimental setup and an engineering solution are proposed to verify the usefulness, reliability, and stability of the proposed ARMA-SDT algorithm in data compression.     

Bearing fault detection utilizing group delay and the Hilbert-Huang transform

Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method.     

Creating an automated chiller fault detection and diagnostics tool using a data fault library

Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.     

Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter

This paper proposes the use of the minimum entropy deconvolution (MED) technique to enhance the ability of the existing autoregressive (AR) model based filtering technique to detect localised faults in gears. The AR filter technique has been proven superior for detecting localised gear tooth faults than the traditionally used residual analysis technique. The AR filter technique is based on subtracting a regular gearmesh signal, as represented by the toothmesh harmonics and immediately adjacent sidebands, from the spectrum of a signal from one gear obtained by the synchronous signal averaging technique (SSAT). The existing AR filter technique performs well but is based on autocorrelation measurements and is thus insensitive to phase relationships which can be used to differentiate noise from impulses. The MED technique can make a use of the phase information by means of the higher-order statistical (HOS) characteristics of the signal, in particular the kurtosis, to enhance the ability to detect emerging gear tooth faults. The experimental results presented in this paper validate the superior performance of the combined AR and MED filtering techniques in detecting spalls and tooth fillet cracks in gears.     

高压输电导线的损伤检测与故障诊断

本文利用红外和电磁传感器分别检测铝绞线和钢芯（钢绞线）的断股故障信号,应用db4小波基对故障信号进行了小波分析,由故障信号的时频域和小波分解细节特征,构造了神经网络诊断模型的输入特征矢量.利用3层BP网络实现了导线断股故障的精确诊断,所研制的红外电磁检测仪器可由巡线机器人携载和操作.     

基于EMD和Hilbert解调的齿轮箱故障诊断

针对复杂的齿轮箱振动信号难以提取出故障特征频率的问题,提出了一种将希尔伯特包络解调技术与经验模式分解(EMD)相结合的分析方法。首先对齿轮箱的故障信号进行了EMD分解,得到了本征模态函数(IMF分量),再对IMF分量进行了包络解调,得到了其调制信号,结合调制信号的频率成分可初步判断出齿轮箱中出现故障的齿轮;然后根据IMF分量与初始信号之间相关系数的大小,选择相关系数较大的分量重构信号,相当于对初始信号进行滤波;最后对重构的信号以啮合频率及其倍频为中心频率进行了带通滤波,对得到的信号进行了包络解调分析,再次进行了故障诊断,以验证故障诊断的准确性。整个过程通过对齿轮箱实测故障信号的分析加以验证。研究结果表明,该方法能够准确地提取出齿轮箱的故障特征频率,从而可以对齿轮箱故障进行有效地诊断。