踏面特征细分

1、不圆

 

 信号平滑处理---Savitzky-Golay：对局部数据进行多项式回归

scipy.signal.savgol_filter(x, window_length, polyorder, deriv=0, delta=1.0, axis=-1, mode='interp', cval=0.0)

from scipy.signal import savgol_filter
x = np.array([2, 2, 5, 2, 1, 0, 1, 4, 9])
y=savgol_filter(x, 5, 2)   # Filter with a window length of 5 and a degree 2 polynomial
plt.plot(y)
plt.show()
z=savgol_filter(x, 5, 2, mode='nearest')
plt.plot(z)
plt.show()

np.percentile(q1, (25, 50, 70, 90))  # 各种分位数
q1.std()

　　

 

 

 

2、内部缺陷

 

3、擦伤

8027时域图像擦伤，频域图像内部多边形

一个交路有很多个波形图，有一些一级报警但是形状并不是很规则，例如

 

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931,3233,4806,6913,8282,7006,6758,3053,9028,13365,9606,16082,12026,5625,11497,12611,15266,10294,8802,6915,0,0,4811,4501,4067,1460,4330,1574,4286,9166,10434,10412,2774,4251,7138,2923,2710,3434,4585,5477,4879,6911,14835,16432,13699,21704,15333,750,4555,3555,433,3097,455,2481,3369,1057,2020,1603,4995,10315,14748,23350,30296,27585,13650,6162,9782,6262,2717,4572,3729,2142,1492,1552,2752,5352,11163,18029,21140,21446,9322,8994,14362,2100,9677,8366,0,5914,1916,1470,6058,1365,4538,6166,8767,14422,11484,8037,9994,7908,2235,3625,3454,3209,3334,2667,5375,2062,426,1809,887,1622,1274,2943,5371,9945,12112,5524,6868,6582,423,3443,3652,1819,1806,152,1477,2340,2744,3669,3743,7548,15974,17339,11270,14776,9956,1619,6363,1771,1464,4545,0,2186,2116,341,3172,3632,3632,7324,7754,4695,2059,4299,4061,3579,2976,2758,3566,2001,2631,2053,692,2501,3749,3836,2564,1816,2169,2206,1467,1118,2096,1799,1096,1971,2272,2084,2976,5949,7915,6504,7417,6692,2853,3683,3750,1535,1860,2829,2667,1672,1582,3190,5670,9588,14670,19089,21329,17617,9031,5852,6197,2829,2669,3418,1421,2533,2782,2950,4541,3388,2936,2197,2935,3926,2694,7283,10866,9737,12474,8897,5103,7620,3862,5321,6156,0,2906,3222,1270,2630,1513,2321,2152,904,1713,2183,2088,2232,2631,2073,1249,2116,3363,3355,3477,3221,2273,2396,2445,2249,2290,1648,1328,1784,2117,1924,1983,2489,2204,1899,2556,2959,2295,1667,1758,1837,1770,1854,1576,1562,2174,2195,1739,1494,1378,1590,2075,2054,1608,1703,1790,1558,1858,1587,1550,2020,2013,2289,1768,1417,1857,1788,1771,1644,1857,1973,1848,2109,2017,2005,1823,1608,1865,1759,2082,1836,1738,2144,1611,1606,1995,2291,3031,1708,1612,3450,2173,1855,2830,2381,2477,2425,2133,1994,1920,2355,1947,1830,2520,2914,3944,3258,2262,3710,3711,2267,1674,1872,2943,2602,1477,1791,2003,1927,2343,2079,2255,2859,2785,2785,2274,2935,7025,8437,8140,5156,3679,9618,11292,6603,3348,2172,3412,4956,3647,2836,5284,6431,7081,10349,16148,19234,14269,13256,22714,19442,12426,18382,12792,5354,4647,3437,4190,3463,4224,5334,4541,7192,8210,9276,12563,10461,17330,17030,9720,16839,7808,3716,7622,3181,6690,3698,3664,6604,4044,5603,5102,7337,11950,12446,8046,5499,7370,7370,10441,9013,4846,7026,5101,3027,4086,3011,4112,4078,3325,4969,5288,6908,7612,8722,9831,5243,5243,4260,2234,4555,2550,1377,2438,2139,4655,6735,6725,9487,12008,8390,5543,4341,990,3357,4236,1269,2888,3274,2981,2747,1739,2840,7674,18743,19479,14315,20900,11121,2391,10549,6221,4662,4846,1830,3171,1847,2449,3554,4040,4552,4168,7821,9538,8606,8303,5594,5481,5366,2004,2262,2979,1522,2368,3214,1979,2916,6120,6982,5528,2982,2510,2256,1887,3306,2373,3829,4978,1581,2274,2813,2340,3470,1826,1642,2171,1833,3133,2320,1710,3446,2441,3140,5426,4115,3732,2901,2037,2445,1137,3266,3931,2409,3965,2648,1830,4806,9708,14795,14658,9512,6388,5887,2241,3419,5858,2215,2072,3656,5240,7508,6915,6083,6371,4414,3346,4996,3615,2388,2784,1810,1648,2656,3016,2476,2439,2496,3765,5688,4322,3684,4705,2966,2598,3222,1731,1983,2049,1507,2484,2687,3018,2734,4915,10757,12338,15192,18029,9671,8151,9537,4103,3134,1470,2981,2911,866,5356,6545,10950,17261,10812,10865,11739,6243,8383,6126,4188,3927,1614,4195,2705,1094,3262,2004,1804,1832,2006,2006,4408,4461,4705,7641,7252,6815,8967,4024,1660,2768,2069,2838,2025,2320,2905,3526,5337,3747,2882,3988,2701,2261,2968,3124,2969,2532,2306,2734,3173,3690,3815,1619,1690,2393,1737,2725,2485,2716,3485,2429,3190,3816,2059,1574,1882,1901,1987,1370,1357,2184,1847,1472,1448,1520,2759,3112,2284,3046,3529,2097,2614,2669,1072,1878,2164,1493,2020,2048,2555,2608,2580,2947,2222,3069,3069,4107,4082,1468,3124,5895,4629,2650,1381,1913,2287,1820,2392,2221,2016,1919,1831,2450,2361,2460,2339,1818,1734,1608,2109,2749,3949,3699,2049,2704,3031,2249,2357,2205,2601,2554,2296,2873,2068,1955,3048,2908,2139,1382,1898,3018,2626,1855,2010,2023,2266,2421,2589,2854,1526,1810,2879,2009,2163,2282,2098,2565,2622,2780,2561,2501,2570,2128,2220,1892,2267,2388,1077,1781,2559,2133,2024,1991,2746,3328,4028,4140,2714,5463,13146,26730,33305,18623,14283,15891,5045,5270,7847,6845,5448,2402,3325,2075,3020,4851,6348,10911,12390,20215,21180,8226,11197,10801,6282,11782,6044,6288,5006,1615,7873,4986,3614,4405,4397,6391,3115,3130,7935,10197,8794,11212,17858,18149,13769,9908,7203,4584,2672,3057,4020,3160,2108,4230,7558,8954,8842,5531,5522,6205,3044,4467,4026,1470,1955,2490,5062,5532,5801,9871,11289,14028,15216,9455,6023,4568,2429,3204,3415,2480,2200,2323,1595,1632,4213,3950,5810,13649,27164,27926,11671,14076,10454,802,802,4000,4210,4185,682,4003,3000,3887,9896,19005,25110,23591,27535,22575,3346,6627,13746,3096,4360,5126,709,3772,2086,2350,5292,5915,4668,3473,6625,7579,7964,5867,3532,5667,3301,2041,4201,7185,10405,8606,4396,3967,5819,3798,1510,1732,1454,5702,11362,15279,19906,12449,4051,7292,3562,123,2690,1234,1902,4155,4196,3421,3105,4534,7725,13290,17918,19239,14581,4974,4580,8860,2780,2546,5250,1900,2889,3082,5807,12387,10902,6602,4112,5685,7807,3258,2486,5216,4054,3926,3196,2129,2987,2633,4217,7041,10133,8929,3995,6052,6606,3687,3401,2108,2569,4321,4003,1381,758,2622,1894,2821,5900,9944,1550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8027 内部缺陷

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8027 非内部缺陷

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# encoding = utf-8
# -*-coding:utf-8-*-

# 没有细分等级报警
import pymysql
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np


def recog(data):
    # 将波形平滑，每20个点取一个平均值
    maxindex = np.argmax(data)
    # 开始测试波动区间是直角还是等腰 ,beginvalue和maxindex不一致怎么办，以maxvalue为准
    begin = int(maxindex+1)
    end = int(400 - maxindex)
    index = [x for x in range(begin, 400)]
    value = data[begin:400]
    df = {"index": index, "value": value}
    df = pd.DataFrame(df)
    df = df.sort_values(["value"], ascending=False).head(int(end*0.6))
    df = df.sort_values(["index"], ascending=True)   # index从小到大
    # ratios = ratio(df['value'].values()) 这行是有用的
    # !!!怎么找到波动停止的地方,比值从后前数小于0.5？
    flag = 0
    endvalue = 240

    if endvalue-maxindex > 60:
        ends = 60
    else:
        ends = abs(endvalue-maxindex)
    index = [x for x in range(begin, maxindex+ends+1)]
    value = data[maxindex+1:maxindex+ends+1]
    df = {"index":index, "value":value}
    df = pd.DataFrame(df)
    df = df.sort_values(["value"], ascending=False).head(40)
    df = df.sort_values(["index"], ascending=False)

    index = [x for x in range(maxindex-ends, begin-1)]
    value = data[maxindex-ends:maxindex]
    df1 = {"index":index, "value":value}
    df1 = pd.DataFrame(df1)
    df1 = df1.sort_values(["value"], ascending=False).head(40)
    df1 = df1.sort_values(["index"], ascending=True)
    diff = abs(df1['value'].values - df['value'].values)
    return diff


def person_value(q):
    # input： 输入4096个振动数据
    # 判断： 10圈数据进行皮尔森相关系数
    # 输出： 0表示正常波形，1表示踏面故障波形
    a = [q[0:400]]
    for i in range(1, 10):
        a.append(test[i*400:400*(i+1)])
    a = np.array(a)
    person = np.corrcoef(a)
    # 对person相关系数进行判断如果存在负数情况
    if len(person[person < 0]) > 10:
        print('负相关波形有五对及其以上，此波形为正常波形')
        flag = 0
    else:
        print('此波形为踏面故障波形')
        flag = 1
    return flag


def filtervalue(index, value, num):
    df = {"index": index, "value": value}
    df = pd.DataFrame(df)
    df = df.sort_values(["value"], ascending=False).head(round(num*len(df)))
    return df


def ratio(value):
    # 前比后的比例
    ratio_value = []
    for i in range(len(value)-1):
        temp = value[i] / value[i+1]
        ratio_value.append(temp)
    return ratio_value


if __name__ == "__main__":
    conn = pymysql.connect(host='10.2.3.72', user='root', passwd='yunda', db='zxb', port=3308, charset='utf8')
    cur = conn.cursor()
    sql = '''SELECT * from tread_subdivision WHERE Alarm=7 AND (ResultMark=16388 or ResultMark=1028)
          '''
    cur.execute(sql)
    # 获取到的是tuple-list-dataframe
    data = pd.DataFrame(list(cur.fetchall()), columns=['ID', 'Cx', 'Ch', 'Routes', 'JiWuDuan', 'AxleNo', 'PlaceNo', 'Speed','Mileage',
                                                      'Alarm', 'AlarmTag', 'OutTemp', 'AxleTemp', 'db', 'sv', 'Data', 'AxleParam',
                                                      'Temp','Datatime','Frequency','ResultMark'])

    test = data.loc[14]['Data'][1:-2].split(',')  # 取的8027机车，二级振动数据
    test2 = data.loc[1]['Data'][1:-2].split(',')  # 取的0029A机车，一级振动数据
    # 将list中的每个str元素转为int,批量转换
    test = list(map(eval, test))
    test2 = list(map(eval, test2))
    persons = person_value(test)  # 返回01值,0为正常波形，1为踏面故障波形

    # 求波形的宽  test3[0:399]/np.max(test3[0:399]) <0.1 且后面的都小于0.1的区域收敛。这种方式问题出在对波峰不高的怎么办？,3分位，5分位，7分位
    q11 = np.array(test[0:400])
    q12 = np.array(test2[0:400])
    # np.percentile(q11, (25, 50, 70, 90, 95))  # 各种分位数
    sql = '''SELECT * FROM tread_subdivision WHERE DataTime='2018-09-09 00:28:45' 
          '''
    cur.execute(sql)
    # 获取到的是tuple-list-dataframe
    data_round = pd.DataFrame(list(cur.fetchall()), columns=['ID', 'Cx', 'Ch', 'Routes', 'JiWuDuan', 'AxleNo', 'PlaceNo', 'Speed','Mileage',
                                                      'Alarm', 'AlarmTag', 'OutTemp', 'AxleTemp', 'db', 'sv', 'Data', 'AxleParam',
                                                      'Temp','Datatime','Frequency','ResultMark'])

    # data_round['Data'] = data_round['Data'].apply(lambda x: x[1:-2].split(',')) 将所有data数据变成列表
    round = data_round.loc[0]['Data'][1:-2].split(',')
    round = list(map(eval, round))
    q13 = np.array(round[0:400])
    # value11 = recog(q11)
    # value12 = recog(q12)
    # value13 = recog(q13)
    flag_count =[]
    for i in range(10):
        q = np.array(test2[i*400:400*(i+1)])
        value = recog(q)
        flag_count.append(len(value[value < 300]))  # 差值小于

    flag_count = np.array(flag_count)
    if len(flag_count[flag_count < 3]) > 5:
        print('次波形为等腰三角形')
    else:
        print('此波形位直角三角形')

 

# encoding = utf-8
# -*-coding:utf-8-*-

'''
1：剥离擦伤
2：内部缺陷
3：多边形
4：椭圆
5：啮合齿异常
6：转速相关频率干扰
7：固定频率干扰
8：故障无法分类
9：正常波形
test[8] 在占比处有问题
'''

import pymysql
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.fftpack import fft,ifft


def person_value(q, n):
    '''
    判断数据是否是踏面故障数据，利用皮尔森相关系数，计算每圈数据是否存在相似性
    :param:q -- 一条振动数据，包括4096个振动点
           :n -- 一圈多少个振动点，一般400个点为一圈
    :return:flag -- 0表示正常波形，1表示踏面故障波形
    '''
    a = [q[0:n]]
    for i in range(1, 10):
        a.append(q[i*n:n*(i+1)])
    a = np.array(a)
    person = np.corrcoef(a)
    # 对person相关系数进行判断如果存在负数情况
    if len(person[person < 0]) > 10:
        print('负相关波形有五对及其以上，此波形为正常波形')
        person_flag = 0
    else:
        print('此波形为踏面故障波形')
        person_flag = 1
    return person_flag


def trangle(q, n):
    '''
    判断波形特征，并计算占空比
    :param:q -- 一条振动数据，包括4096个振动点
           :n -- 设置一圈多少个振动点，一般一圈有400个点
    :return:yf2 -- 占空比,是算其中一圈还是所有的圈？？？ 最小的值作为占空比！！！
            :trangleflag --是否是等腰还是直角,等腰是1，直角是0
    '''
    trangle_flag = 1
    zhankongbi = []
    trangle_count = []
    q = std(q, 10)  # 用std处理数据，减小振荡 此时数据宽度变为step
    step = int(n/10)
    for i in range(0, len(q)//n):
        oldlist = q[i*step:(i+1)*step]
        maxvalue = max(oldlist)
        maxindex = oldlist.index(max(oldlist))
        newlist = np.array(oldlist)/maxvalue
        # 将小于0.2的数据作为占空比 ,找到最小和最大的index，作为波形波动的范围区间 ，从最大值左右搜索到很小的值的宽度作为占空比是最好的，但是值得大小不一定，max/value
        #
        zhankongbi.append((max(max(np.where(newlist > 0.2)))-min(min(np.where(newlist > 0.2))))/step)
        #
        # if max(max(np.where(newlist > 0.2)))-maxindex>maxindex-min(min(np.where(newlist > 0.2))) and :

        if (max(max(np.where(newlist > 0.2)))-maxindex)/(maxindex-min(min(np.where(newlist > 0.2)))) > 3:
            trangle_count.append(0)
        else:
            trangle_count.append(1)
    # 10圈三角形统计，直角三角形个数大于等腰三角形，那么把此波形判定为直角三角形
    if trangle_count.count(0) > trangle_count.count(1):
        trangle_flag = 0
    else:
        trangle_flag = 1
    return min(zhankongbi), trangle_flag


def time2frequency(q):
    '''
    时域转频域，并判断在15-35之间是否存在冲击值凸起
    :param:q -- 一条振动数据，包括4096个振动点
    :return:yf2 -- 频域振动数据
            :roundflag --0表示没有凸起，1表示在15-35阶处有凸起
    '''
    roundflag = 0
    yf1 = abs(fft(q))/((len(q)/2))
    yf2 = yf1[range(int(len(q) / 2))]
# 判断是否有凸起
    df = pd.DataFrame({"index": [x for x in range(len(yf2))], "value": yf2})
    df = df.sort_values(["value"], ascending=False).head(20)
    df = df.reset_index(drop=True)
    if df['index'].isin([x for x in range(150, 350)]).any() and \
            df[df['index'].isin([x for x in range(150, 350)])].index.isin([x for x in range(0, 15)]).any():
        print('此条数据在%s阶处有凸起',df[df['index'] > 150].head(1)['index'].values)
        roundflag = 1
# 判断是否一直是下降
    return roundflag


def std(value, step):
    '''
   对波形进行方差处理
    :param:value-- 一条振动数据，包括4096个振动点
            step -- 多少条数据进行一次方差求值
    :return:yf2 -- 返回间距值间的方差
    '''
    result = []
    for i in range(0, len(value), step):
        result.append(np.std(value[i:i+10]))
    return result


def standard_400(q):
    '''
    判断振动数据是否为0-400一圈的标准型，若不是算出平移量
    :param:q -- 一条振动数据，包括4096个振动点
    :return:flush -- 平移偏量
    '''
    a = [q[0:400]]
    flush = []
    for i in range(1, 10):
        a.append(q[i * 400:400 * (i + 1)])
    a = np.array(a)
    for i in range(10):
        df = pd.DataFrame({"index": [x for x in range(400)], "value": a[i]})
        df = df.sort_values(["value"], ascending=False).head(50)
        df = df.reset_index(drop=True)
        if df['index'].isin([x for x in range(370, 400)]).any()  \
                and df['index'].isin([x for x in range(370, 400)]).index.isin([x for x in range(0, 350)]).any():
            print('此条数据有偏移，现在开始计算偏移量')
            result = std(a[i], 10)
            flush.append(result.index(min(result)))   # 由第一个400圈就可以判定出来
        else:
            print('此条数据是标准400型数据')
    try:
        flush.remove(min(flush))
        flush.remove(max(flush))
        flush = int(round(np.mean(flush)))  # 取平均值作为偏移量
    except:
        flush = []
    return flush


def get_data():
    '''
    获取数据，现在主要从数据库中提取特征数据
    :return:data
    '''
    conn = pymysql.connect(host='10.2.3.72', user='root', passwd='yunda', db='zxb', port=3308, charset='utf8')
    cur = conn.cursor()

    #
    # sql = ''' SELECT * FROM tread_subdivision where AlarmTag >0
    # '''

    sql = '''
    SELECT * FROM tread_subdivision where Ch='0189A' and DataTime between  '2016-12-09 01:32:53' and '2016-12-09 05:24:43'
    and AlarmTag >0
    '''
    # 这两条主要是非400标准型
    # sql = '''SELECT * from tread_subdivision WHERE DataTime='2018-09-20 12:05:14' or DataTime='2018-09-21 12:57:41'
    #       '''
    cur.execute(sql)
    # 获取到的是tuple-list-dataframe
    data = pd.DataFrame(list(cur.fetchall()), columns=['ID', 'Cx', 'Ch', 'Routes', 'JiWuDuan', 'AxleNo', 'PlaceNo', 'Speed','Mileage',
                                                      'Alarm', 'AlarmTag', 'OutTemp', 'AxleTemp', 'db', 'sv', 'Data', 'AxleParam',
                                                      'Temp','Datatime','Frequency','ResultMark'])

    return data


if __name__ == "__main__":
    data = get_data()
    round_data = 400
    data['predit'] = None
    # 判断波形是否正常，若表现为踏面故障对称波形则进行进一步判断    返回01值,0为正常波形，1为踏面故障波形
    test = data['Data'].apply(lambda x: x[1:-2].split(','))
    for i in range(len(test)):
        if ' ' in test[i]:
            index = test[i].index(' ')
            test[i][index] = '1'
        test[i] = list(map(eval, test[i]))  # 将list中的每个str元素转为int,批量转换
        persons = person_value(test[i], round_data)
        if persons == 0:
            data['predit'][i] = 9
        else:
            print('第%s条数据为踏面故障数据，继续进行特征分解',i)
            flush = standard_400(test[i]) * 10  # 判断是否有偏移，非0-400标准型  返回偏移量
            if flush:
                test_new = test[i][flush:]
            else:
                test_new = test[i]
            roundflag = time2frequency(test[i])  # 是否有多阶，对于直角三角形但是存在凸起，仍判定为多边形
            zhanbi, flag = trangle(test_new, round_data)
            if roundflag == 1:
                print('此条振动数据为多边形')
                data['predit'][i] = 3
            elif flag == 0 and roundflag== 0:
                print('此条振动数据为内部缺陷')
                data['predit'][i] = 2
            elif flag == 1 and roundflag == 0:
                print('此条振动数据为擦伤')
                data['predit'][i] = 1