DolphinDB振动分析：设备振动信号处理

摘要

本文深入讲解DolphinDB振动分析技术。从振动信号原理到数据采集，从时域分析到频域分析，从故障诊断到实战应用，全面介绍设备振动信号处理的核心方法。通过丰富的代码示例，帮助读者掌握振动分析的核心技能。

一、振动分析概述

1.1 振动信号原理

1.2 振动参数

1.3 振动标准

二、数据采集

2.1 采集配置

//振动数据采集配置 vibrationConfig=dict(STRING,ANY,[["sample_rate",10000],//采样率 10kHz["sample_time",1],//采样时间1秒["channels",["CH1","CH2","CH3","CH4"]],//采集通道["sensitivity",100],//传感器灵敏度 mV/g["range",50]//量程 ±50g])

2.2 数据存储

//创建振动数据流 share streamTable(1000000:0,`device_id`timestamp`channel`acceleration,[SYMBOL,TIMESTAMP,STRING,DOUBLE])asvibration_stream enableTablePersistence(vibration_stream,true,true,10000000)//创建振动特征表 share table(1:0,`device_id`timestamp`channel`rms`peak`crest_factor`kurtosis,[SYMBOL,TIMESTAMP,STRING,DOUBLE,DOUBLE,DOUBLE,DOUBLE])asvibration_features

2.3 数据预处理

//数据预处理defpreprocessVibration(data,config){//1.去除直流分量 dc=avg(data)data=data-dc//2.去趋势 trend=linreg(1..data.rows(),data)[0]data=data-trend*(1..data.rows())//3.滤波（简化版）//实际应用中使用带通滤波器 filtered=mavg(data,5)returnfiltered}

三、时域分析

3.1 基本统计量

//时域统计特征deftimeDomainFeatures(data){//均值 mean=avg(data)//均方根值 rms=sqrt(avg(data*data))//峰值 peak=max(abs(data))//峰峰值 peakToPeak=max(data)-min(data)//标准差 std=std(data)//峰值因子 crestFactor=peak/rms//峭度 kurt=kurtosis(data)//偏度 skew=skewness(data)returndict(STRING,ANY,[["mean",mean],["rms",rms],["peak",peak],["peak_to_peak",peakToPeak],["std",std],["crest_factor",crestFactor],["kurtosis",kurt],["skewness",skew]])}

3.2 波形指标

//波形指标计算defwaveformIndicators(data){//均方根值 rms=sqrt(avg(data*data))//绝对平均值 absMean=avg(abs(data))//方根幅值 sqrtMean=sqrt(avg(sqrt(abs(data))))^2//波形指标 shapeFactor=rms/absMean//脉冲指标 impulseFactor=max(abs(data))/absMean//裕度指标 clearanceFactor=max(abs(data))/sqrtMeanreturndict(STRING,ANY,[["shape_factor",shapeFactor],["impulse_factor",impulseFactor],["clearance_factor",clearanceFactor]])}

3.3 趋势分析

//振动趋势分析defvibrationTrend(data,window=100){returnselect timestamp,vibration,mavg(vibration,window)astrend,mstd(vibration,window)asvolatility,vibration-mavg(vibration,window)asdeviationfromdata context by device_id}

四、频域分析

4.1 FFT变换

//FFT变换（简化版）deffftAnalysis(data,sampleRate){n=data.rows()//实际应用中使用DolphinDB的fft函数//这里展示计算过程//频率分辨率 freqResolution=sampleRate*1.0/n//频率轴 frequencies=0..(n/2)*freqResolution//幅值谱（简化）//实际需要FFT计算 magnitudes=abs(data)returndict(STRING,ANY,[["frequencies",frequencies],["magnitudes",magnitudes],["freq_resolution",freqResolution]])}

4.2 频谱特征

//频谱特征提取defspectrumFeatures(spectrum,freqBands){features=dict(STRING,DOUBLE)for(bandinfreqBands.keys()){range=freqBands[band]//计算频带能量 energy=sum(spectrum[(spectrum.frequencies>=range[0])&(spectrum.frequencies<range[1])].magnitudes^2)features[band]=energy}//总能量 features["total_energy"]=sum(spectrum.magnitudes^2)//重心频率 features["centroid_freq"]=sum(spectrum.frequencies*spectrum.magnitudes)/sum(spectrum.magnitudes)returnfeatures}

4.3 特征频率

//转子故障特征频率defbearingFaultFrequencies(rpm,bearingParams){//转频 f_r=rpm/60.0//滚动体数量 n=bearingParams["ball_count"]//接触角 alpha=bearingParams["contact_angle"]*pi/180//节径 d=bearingParams["pitch_diameter"]//滚动体直径 ball_d=bearingParams["ball_diameter"]//内圈故障频率 f_inner=f_r*n/2*(1+ball_d/d*cos(alpha))//外圈故障频率 f_outer=f_r*n/2*(1-ball_d/d*cos(alpha))//滚动体故障频率 f_ball=f_r*d/(2*ball_d)*(1-(ball_d/d*cos(alpha))^2)//保持架故障频率 f_cage=f_r/2*(1-ball_d/d*cos(alpha))returndict(STRING,DOUBLE,[["rotation_freq",f_r],["inner_race_freq",f_inner],["outer_race_freq",f_outer],["ball_freq",f_ball],["cage_freq",f_cage]])}

五、故障诊断

5.1 故障特征库

//故障特征库 faultLibrary=dict(STRING,ANY,[["不平衡",dict(STRING,ANY,[["特征频率","1X转频"],["时域特征","正弦波形"],["频域特征","转频处有明显峰值"]])],["不对中",dict(STRING,ANY,[["特征频率","2X转频"],["时域特征","M形波形"],["频域特征","2倍转频处有明显峰值"]])],["轴承故障",dict(STRING,ANY,[["特征频率","内圈/外圈/滚动体频率"],["时域特征","冲击脉冲"],["频域特征","高频段能量增加"]])],["松动",dict(STRING,ANY,[["特征频率","多倍频"],["时域特征","不稳定波形"],["频域特征","丰富的高次谐波"]])]])

5.2 故障识别

//故障识别defidentifyFault(features,faultFreqs,rpm){faults=array(STRING,0)//转频 f_r=rpm/60.0//检查不平衡if(features["1x_amplitude"]>features["rms"]*0.5){faults.append!("不平衡")}//检查不对中if(features["2x_amplitude"]>features["rms"]*0.3){faults.append!("不对中")}//检查轴承故障if(features["high_freq_energy"]>features["total_energy"]*0.2){faults.append!("轴承故障")}//检查松动if(features["harmonic_count"]>5){faults.append!("松动")}returnfaults}

5.3 严重程度评估

//故障严重程度评估defassessSeverity(features,thresholds){//ISO10816振动速度标准 rms=features["rms"]if(rms<thresholds["good"]){severity="良好"level=1}elseif(rms<thresholds["satisfactory"]){severity="满意"level=2}elseif(rms<thresholds["unsatisfactory"]){severity="不满意"level=3}else{severity="不可接受"level=4}returndict(STRING,ANY,[["severity",severity],["level",level],["rms",rms]])}

六、实战案例

6.1 完整振动分析系统

//==========振动分析系统==========//1.创建数据表 share streamTable(1000000:0,`device_id`timestamp`channel`acceleration,[SYMBOL,TIMESTAMP,STRING,DOUBLE])asvibration_stream enableTablePersistence(vibration_stream,true,true,10000000)//2.创建分析结果表 share table(1:0,`device_id`timestamp`channel`rms`peak`crest_factor`kurtosis`fault_type`severity,[SYMBOL,TIMESTAMP,STRING,DOUBLE,DOUBLE,DOUBLE,DOUBLE,STRING,STRING])asvibration_analysis//3.振动分析函数defanalyzeVibration(deviceId,channel,data){//时域特征 timeFeatures=timeDomainFeatures(data)//故障识别 faults=identifyFault(timeFeatures,null,1500)//严重程度 severity=assessSeverity(timeFeatures,dict(STRING,DOUBLE,[["good",1.8],["satisfactory",4.5],["unsatisfactory",11.2]]))returndict(STRING,ANY,[["rms",timeFeatures["rms"]],["peak",timeFeatures["peak"]],["crest_factor",timeFeatures["crest_factor"]],["kurtosis",timeFeatures["kurtosis"]],["fault_type",faults.size()>0? faults[0]:"正常"],["severity",severity["severity"]]])}//4.分析任务defanalysisTask(){while(true){now=now()//获取最新数据 data=select*fromvibration_stream where timestamp>now-60000if(data.rows()>0){//按设备和通道分组分析for(deviceIdinexecdistinct device_idfromdata){for(channelinexecdistinct channelfromdata where device_id=deviceId){//获取通道数据 channelData=execaccelerationfromdata where device_id=deviceIdandchannel=channel order by timestampif(channelData.size()>=100){//分析 result=analyzeVibration(deviceId,channel,channelData)insert into vibration_analysis values(deviceId,now,channel,result["rms"],result["peak"],result["crest_factor"],result["kurtosis"],result["fault_type"],result["severity"])}}}}sleep(60000)}}//5.启动系统 submitJob("vibration_analysis","振动分析",analysisTask)//6.查询接口defgetVibrationAnalysis(deviceId){returnselect*fromvibration_analysis where device_id=deviceId order by timestamp desc limit10}defgetFaultDevices(){returnselect device_id,fault_type,severityfromvibration_analysis where timestamp>now()-300000andfault_type!="正常"}addFunctionView(getVibrationAnalysis)addFunctionView(getFaultDevices)print("振动分析系统启动完成")

七、总结

本文详细介绍了DolphinDB振动分析：

1. 振动信号原理：振动参数、振动标准
2. 数据采集：采集配置、数据存储、数据预处理
3. 时域分析：统计量、波形指标、趋势分析
4. 频域分析：FFT变换、频谱特征、特征频率
5. 故障诊断：故障特征库、故障识别、严重程度

思考题：

1. 如何选择合适的振动分析参数？
2. 如何提高故障诊断的准确性？
3. 如何实现振动分析的实时处理？

参考资料

• DolphinDB信号处理
• ISO 10816振动标准