机械特性测试

需求

• 读取伺服电机反馈电流参数
• 分析参数确定最佳的适用工作区间

方法

• 利用伺服电机配套控制软件导出运行参数
• python分析处理数据，绘制图形
• 进行平均和差分时使用了滑动窗口

结果

Figure_1.png

2.png

附

#! -*- coding: utf-8 -*-

import pandas as pd 
import csv 
import matplotlib.pyplot as plt

import sys

def skipFile( inFile, r, outFile, maxLine=7680 ):
    with open( outFile, 'w') as csvout:
        writer = csv.writer( csvout, lineterminator='\n' )            
        with open( inFile, 'r' ) as csvfile:
            reader = csv.reader( csvfile )
            iNow = 0 
            rowSum = 0
            for line in reader:
                if ( iNow < r ):
                    pass 
                else:
                    # write to the file 
                    if ( rowSum >= maxLine ):
                        break

                    writer.writerow( ( line ) )
                    rowSum += 1 

                    # print( line )
                    # print( iNow, line )

                iNow += 1

# sum range
def sumRange( data, rng ):
    thisSum = 0.0
    for i in range( rng[0], rng[1] ):
        thisSum += data[i]
    return thisSum 

# rng = [x, y)
def avgRange( data, rng ):
    thisSum = sumRange( data, rng )
    return thisSum / ( rng[1] - rng[0] )                

def windowFilter( data, wnd, dataOut ):
    dataLen = len( data )
    halfWnd = int(wnd/2)

    print( dataLen )

    for i in range(  halfWnd, dataLen - halfWnd ):
        dataOut[ i ] = avgRange( data, ( i-halfWnd, i - halfWnd + wnd ) )

    # fill the head
    for i in range( 0, halfWnd ):
        dataOut[i] = dataOut[ halfWnd]

    # fill the tail
    for i in range( dataLen-halfWnd, dataLen ):
        dataOut[ i ] = dataOut[ dataLen-halfWnd-1 ]


def diffFilter( data, wnd, dataOut ):
    dataLen = len( data )
    halfWnd = int(wnd/2)

    print( dataLen )

    for i in range(  halfWnd, dataLen - halfWnd ):
        a = sumRange( data, ( i - halfWnd, i ) )
        b = sumRange( data, ( i, i + halfWnd ) )
        dataOut[ i ] = ( b - a ) / halfWnd

    # fill the head
    for i in range( 0, halfWnd ):
        dataOut[i] = dataOut[ halfWnd]

    # fill the tail
    for i in range( dataLen-halfWnd, dataLen ):
        dataOut[ i ] = dataOut[ dataLen-halfWnd-1 ]        
import os 
if __name__=="__main__":
    if len(sys.argv)>1:
        print( sys.argv )
        print( os.path.abspath(os.path.curdir) )

        filePath = sys.path[0]
        print( filePath )
        # skipFile( 'E:/work/2.csv',
        skipFile( sys.argv[1],
                5, 
                filePath + '/out.csv' )
    else:
        exit( 0 )            

    csv_data = pd.read_csv( filePath + '/out.csv', header=None )
    print( csv_data.shape )

    totalSum = max( csv_data[0] )
    print( totalSum )

    dataOut = [ x for x in range( len( csv_data[2] ) ) ]
    windowFilter( csv_data[2].tolist(), 200, dataOut )

    dataDiff = [ x for x in range( len( csv_data[2] ) ) ]
    diffFilter( dataOut, 500, dataDiff )

    plt.plot( csv_data[0]* 360/totalSum, csv_data[2], label='current' )
    plt.plot( csv_data[0]* 360/totalSum, dataOut, label='average' )
    plt.plot( csv_data[0]* 360/totalSum, dataDiff, label='diff' )

    plt.xlabel( 'Degree') 
    plt.ylabel( 'Current')
    plt.grid( which='both')
    plt.legend( ['current', 'average', 'diff'] )
    plt.gca().set_xticks( [ tick for tick in range( 0,360,10) ], True )

    plt.show()