基于DHMM的轴承故障音频诊断方法

轴承音频信号包含了大量的运行状态信息,与振动信号相比,音频信号的采集是非接触式的,具有使用方便和成本低廉等优势。通过提取机械轴承音频信号的Mel频率倒谱系数(MFCC)特征参数,并使用具有良好识别和抗噪性能的隐马尔可夫模型(HMM)分析轴承运行状态,首次将HMM对音频信号的分析方法应用于故障诊断。为了能够实现对轴承故障的实时诊断,采用计算量较小的离散HMM(DHMM)模型加快训练和识别速度。实验结果表明,该方法对轴承运行状态的识别正确率接近90%,识别时间约为31ms,效果较好,有效可行,具有很好的应用前景。     

轴承故障音频诊断系统设计与开发

轴承音频信号包含了其运行状态的许多重要信息,通过这些信息的恰当分析、处理就能对轴承故障进行有效诊断.与振动信号相比,音频信号的采集是非接触式的,具有使用方便、成本低廉等优势.因此,将基于音频信号的隐马尔可夫模型(Hidden Markov Model,HMM)建模方法引入到轴承故障诊断研究,以PC机为平台,在Visual C 7.0环境下设计开发了一套功能完善、操作方便、界面友好的轴承音频故障诊断系统.主要介绍了诊断系统的体系结构,HMM方法建模的步骤和故障诊断过程中的具体应用操作.多次实验结果表明,效果良好.     

基于单状态HMM的音频分类方法研究

经典的隐马尔可夫模型(HMM)是一种基于统计信号的模型,它在基于内容的音频检索系统中具有重要的作用。根据音频分类重类型轻内容的特性,将单状态的HMM用于音频分类,克服了多状态HMM在模型初始化时状态初始概率和转移概率赋值带有假设不准确的缺点。实验结果表明基于单状态的HMM模型音频分类方法能有效地减少误识率,提高音频分类的精确度。     

一种自适应报警的轴承性能退化评估新方法

论文首次将线性预测倒谱系数(LPCC)引入到轴承声信号特征提取,并提出了一种自适应报警的轴承声信号性能退化评估新方法:首先使用正常状态下的声信号进行隐马尔可夫模型(Hidden Markov Model,HMM)训练,建立正常HMM模型;然后计算当前评估数据在正常HMM模型条件下的输出概率,当输出概率超出阈值范围时,即认定轴承性能状态处于退化Ⅰ级.在启动报警的同时采集退化Ⅰ级声信号重新训练HMM,使用新的HMM对下一时刻运行的轴承进行性能退化评估,直到依次出现退化Ⅱ级,退化Ⅲ级或更严重的退化状态,实现了自适应性能退化评估功能.轴承加速疲劳寿命实验表明,论文提出的自适应性能退化评估方法能够描述轴承整个性能退化过程,具有良好地应用前景.     

基于FPGA的便携式轴承状态监测仪设计

针对目前包络检波算法计算复杂且不适宜在便携式设备上进行轴承监测诊断的问题,介绍了一种基于FPGA的便携式轴承状态监测仪的设计方案;仪器采用FPGA作为主控制器,通过模拟信号放大电路、滤波电路和包络检波电路等硬件电路实现了包络信号的提取、检波功能,并使用FPGA的IP核对包络信号进行DFT运算得到频谱信息,最后通过液晶屏进行结果显示,为轴承状态监测诊断提供依据;文章仪器基于FPGA的嵌入式架构,通过硬件实现包络检波功能,具有运算速度快、便携性强、低功耗、高性能的优点.     

基于CGHMM的轴承故障音频信号诊断方法

轴承音频信号包含其运行状态的重要信息,通过分析这些信息就能对轴承故障进行有效诊断。率先引入基于连续高斯混合密度隐马尔可夫模型的轴承故障音频诊断方法,避免矢量量化带来的数据处理误差,提高了系统诊断精度;引入基于聚类算法的模型参数初始化方法和标定系数的前向-后向算法,简化系统复杂度,加快了训练和诊断速度,进一步提高了诊断精度。实验结果表明,诊断精度达到98.75%,具有很好的应用前景。     

基于频谱细化技术的精密旋转轴承音频信号故障检测方法研究

本文研究了基于复解析带通滤波器的复调制频谱细化方法（一种优化ZFFT算法）在精密旋转轴承音频信号故障检测上的应用;针对音频信号的特点,采用加汉宁窗三点卷积幅值校正法进行了误差校正.通过实验证明：该频谱细化技术可以得到高精度的音频信号频率细化谱线,为精密旋转轴承音频信号频谱的细化分析、特征提取、故障检测提供了一种简单有效的方法.     

DHMM在机械设备音频识别中的应用

为了对现场机械或设备进行监控、诊断和识别,以音频为监控手段,引入矢量量化(VQ)算法并建立机械设备音频的离散隐Markov模型(DHMM)。特征参数采用MFCC,码书设计采用Linde-Buzo-Gray(LBG)算法;推导出Baum-Welch算法参数重估的多观察序列的最简标定形式;分析了多种HMM类型,提出了适合机械设备音频的HMM。实验在22种音频中进行,识别准确率在97%以上,证明了方法的有效性。     

基于DSP与TLV320AIC23B的音频处理系统

介绍了基于DSP的音频处理技术,提供采用音频编解码芯片TLV320AIC23和DSP理器实现的音频处理系统的典型解决方案.音频编解码芯片完成模拟音频信号与数字信号之间的相互转换,包括语音信号采集和语音信号发送两部分.DSP处理器则完成对经模数转换后的语音信号在数字域处理的过程.该方案可以充分发挥DSP所具有的灵活性好、处理速度快的特点.     

基于乘积HMM的双模态语音识别方法

针对噪声环境中的语音识别,提出一种用于双模态语音识别的乘积隐马尔可夫模型(HMM)。在独立训练音频HMM和视频HMM的基础上,建立二维训练模型,表征音频流和视频流之间的异步特性。引入权重系数,根据不同噪声环境自适应调整音频流与视频流的权重。实验结果证明,与其他双模态语音识别方法相比,该方法的识别性能更高。     

基于GA改进DHMM和KPCA-RS的滚动轴承智能诊断方法研究

为实现滚动轴承故障智能诊断,提出了一种基于核主元分析法(KPCA)、粗糙集(RS)和遗传算法(GA)改进离散隐马尔科夫模型(DHMM)的智能诊断方法。通过使用混合核函数的KPCA和RS对时域、频域参数进行约简,构造敏感性高、稳定性强,并能准确表征轴承状态的特征参数矩阵。应用GA优化了DHMM,克服了DHMM训练算法容易陷入局部极小的缺点。最后应用GA优化的DHMM训练算法得到的滚动轴承各状态下的DHMM,并通过比较测试样本在各DHMM下的对数似然概率,实现了轴承故障类型的有效识别。实验结果表明,该方法可以有效地识别滚动轴承的状态,具有较强的适用性。     

列车轴承故障声学信号采集系统设计

目前大部分的声学信号轴承故障诊断系统是基于传声器与轴承位置相对固定的情况设计的,针对该方案每个轴承都要配备一个传声器,不适用于列车轴承故障诊断的缺点,提出了一种能让一个传声器同时应用于多个轴承故障数据采集的设计方案.首先,以TMS320F2812DSP为主控芯片设计了列车车速信息采集计算系统.其次,基于PCI8622型数据采集卡设计了轴承故障声学信号采集系统.最后,基于Visual Studio 6.0设计了人机交互系统.实验结果表明,该文设计的声学信号采集系统能够实现对轴承故障数据的采集及存储,具有可靠性高、人机界面友好等优点.     

A new approach based on discrete hidden Markov model using Rocchio algorithm for the diagnosis of the brain diseases

Transcranial Doppler (TCD) study of the adult intracerebral circulation has gained an important popularity in last 10 years, since it is a non-invasive, easy to apply and reliable technique. In this study, an implementation on biomedical system has been developed for classification of signals gathered from middle cerebral arteries in the temporal area via TCD for 24 healthy and 82 ill people which have one of the four different brain patients such as; cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. Basically, the system is composed of feature extraction and classification parts. In the feature extraction stage, the Linear Predictive Coding (LPC) Analysis and Cepstral Analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage a new Discrete Hidden Markov Model (DHMM) based approach was proposed for the diagnosis of brain diseases. This proposed method was developed via Rocchio algorithm. Therefore, to calculate DHMM parameters regulated according to maximum likelihood (ML) approach, both training samples of related class and other classes were included in calculation. Thus, DHMM model parameters presenting one class were suggested to represent the training samples related to that class better as well as not to represent the training samples related to other classes. The performance of the proposed DHMM with Rocchio approach was compared with some methods such as DHMM, Artificial Neural Network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods.     

基于声发射参数分析的滑动轴承故障诊断方法研究

本文基于声发射产生机理,对基于声发射参数分析法进行滑动轴承故障诊断方法进行理论和实验研究。首先,通过汽轮机发电机组模拟转子实验台模拟了滑动轴承三种润滑状态,通过实验台以及设计的实验方案,利用声发射采集设备对不同润滑状态的声发射信号进行采集。其次,针对采集到的不同润滑状态声发射信号,对其能量均值以及功率谱熵均值进行计算,提出了基于声发射能量均值和功率谱熵均值的散度指标的滑动轴承润滑状态诊断方法,并利用这种方法对模拟信号进行诊断,同时将其与单一能量参数分析法进行对比,发现能量参数分析法不能很好的反映出滑动轴承的三种润滑状态,而文中所提的采用多参数结合的指标诊断方法具有更好的信号适应性以及更高的区分度。     

Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals

When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals.     

基于小波包和EMD的HHT时频分析方法在航空试验器轴承故障诊断中的应用

为适应高转速要求,航空试验器轴承通常选用陶瓷的球体和复合材料的保持架。这种轴承发热量小,同时保持架材料具有轻且脆的结构特点。轴承振动经过试验器传递到振动传感器后,常规的振动采集与温度监控都很难识别出有效的轴承故障信息,无法对轴承故障进行准确预判。针对这一问题,提出一种基于小波包、经验模态分解（EMD）和Hilbert-Huang变换（HHT）组合的轴承振动信号分析方法。首先,通过小波包对振动噪声的抑制作用,经由EMD方法,对非平稳信号进行平稳化处理;之后,通过HHT时频分析提取出轴承的故障频率。通过将仿真信号和航空试验器的高速工装轴承的故障试验信号进行对比分析,验证了该技术对提取该类轴承故障特征的有效性,可为轴承故障早期诊断方法的研究提供参考。     

Research on fault diagnosis of bearings in walking part of wall-building robot based on roadside acoustic signal

The walking part of the wall-building robot is an important component whose health influences the robot's overall performance, and the bearing of the walking part is a critical but often disregarded component. As a result, based on acoustic signals from the roadside, this study proposes a method of bearing fault monitoring of walking parts to ensure the more stable operation of the wall-building robot. To begin, due to the Doppler effect caused by the relative displacement between the sensor and the walking part, the collected acoustic signal has frequency shift and amplitude attenuation, so the multiple signal classification spectrum estimation method with added windows is first used to determine the position and speed of the walking part bearing in real time, and then obtain a resampled time series of the distorted signal. Second, the distorted signal is resampled by Morse acoustic theory to correct the distorted sound signal collected by the array sensor. Third, a multitask deep learning method with an attention mechanism is designed to jointly diagnose the fault type and damage degree of bearing by analyzing the corrected acoustic signal. The experimental results show that the method can effectively restore the distorted signal while still accurately and stably detecting the fault type and damage degree of the bearing under a variety of working conditions and external disturbances, making it more suitable for the work of wall-building robots.