对PHM铣刀磨损数据进行分析

小咪咪 2022-05-13 14:50 248阅读 0赞

试验数据来源于美国纽约预测与健康管理学 会（ＰＨＭ）２０１０年高 速 数 控 机 床 刀 具 健 康 预 测 竞 赛的开放数据。

实验条件如表格 所示 。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70][]

实验数据获取的形式是： 试验在上述切削条件下重复进行 ６ 次全寿命周期试验。端面铣削材料为正方形， 每次走刀端

面铣的长度为 １０８ｍｍ  且 每 次 走 刀 时 间 相 等 ， 每次走刀后测量刀具的后刀面磨损量。试验监测数据有ｘ、ｙ 、 ｚ 三向

铣削力信号 ， ｘ 、 ｙ 、 ｚ 三向铣削振动信号以及声发射均方根值。

6次的数据集中 3次实验中有测量铣刀的磨损量，其他3次没有测量，作为比赛的测试集。

系统测量的实验条件和实验方式如下所示：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70 2][]

获取铣刀数据集的下载链接，包含6次走刀的数据集 和 相关使用该数据集发表的中英文论文

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70 3][]

#coding=gbk
    
    import pandas as pd 
    import numpy as np
    import matplotlib.pyplot as plt
    import os
    import time 
    #新的数据集中的全部数据集
    sta = time.clock()
    path = r'D:\ml_datasets\PHM\c6_new'
    
    class Plot_image():
        def __init__(self, path):
            self.path = path
            self.start = time.clock()
        
        def __str__(self):
            end = time.clock()
            return 'spending time : %.3f'%(end-self.start) + ' s'
        
        def get_all_file(self): #过去文件夹中的所有文件名称
            files =os.listdir(path)
            files.sort(key=lambda x:int(x[:-4])) 
            s= []
            for file in files:
                if not  os.path.isdir(path +file):  #判断该文件是否是一个文件夹       
                    f_name = str(file)  #        
        #             print(f_name)
                    tr = '\\'
                    filename = path + tr + f_name        
                    s.append(filename)  # 把当前文件名返加到列表里
            return s
            
        def get_data(self, i):  #获得相应的数据
            list = self.get_all_file()
            data = pd.read_csv(list[i-1])
            return data
        
        def get_shape(self, n=2):   # n为获取多少 csv 文件的 shape
            k = 0
            print('-------start output shape------')
            for i in range(1, n+1):
                list= self.get_all_file()
                data = pd.read_csv(list[i-1])
                print(str(i) +' shape is: ' + str(data.shape), end=' ; ')
                k += 1
                if k%3==0:
                    print()
            print()
            print('-------output done------')
            
        def get_max_min_shape(self, n=2):
    #         k = np.empty((315, 1))
            print('-------start output shape------')
    #         for i in range(n):
    #             list= self.get_all_file()
    #             data = pd.read_csv(list[i])
    #             shape = data.shape
    #             k[i] = shape[0]
            list= self.get_all_file()
            k =[pd.read_csv(list[i]).shape[0] for i in range(n)]
            k = np.array(k)
            max = np.max(k)
            min = np.min(k)
            mean = np.mean(k)
            print('max : %d, min: %d, mean: %d'%(max, min, mean)) 
            print('-------output done------')
        
        def get_shape_num(self, n=2):
            k = 0
            print('-------start output shape number------')
            for i in range(1, n+1):
                list= self.get_all_file()
                data = pd.read_csv(list[i-1])
                shape = data.shape 
                k += shape[0]
            print('all shape rows sum is : %d'%(k))
            print('-------output shape number done------')
            
        def get_file_feature(self, i=1, column_num = 0): # 获取单一表格中的10个特征
            li = []
            data = self.get_data(i=i)
            data = data.iloc[:, column_num] #1.使用全部数据集，2.使用10万个数据量
            size = data.size
            #最大值
            max = np.max(data)
            li.append(max)
            #均方根值
            root_mean_score = np.sqrt(np.sum(np.square(data)) / size)
            li.append(root_mean_score)
            #歪度值
            skewness = np.sum(np.power(data, 3)) / size
            li.append(skewness)
            #峭度值
            Kurtosis_value = np.sum(np.power(data, 4)) / size
            li.append(Kurtosis_value)
            #波形指标
            absolute_mean_value = np.sum(np.fabs(data)) / size #绝对平均值
            shape_factor = root_mean_score / absolute_mean_value
            li.append(shape_factor)
            #脉冲指标
            pulse_factor = max / absolute_mean_value
            li.append(pulse_factor)
            #歪度指标
            Kurtosis_factor = Kurtosis_value / root_mean_score
            li.append(Kurtosis_factor)
            #峰值指标
            crest_factor = max / root_mean_score
            li.append(crest_factor)
            #裕度指标
            Root_amplitude = np.square(np.sum(np.sqrt(np.fabs(data))) / size)   #方根幅值
            clearance_factor = max / Root_amplitude
            li.append(clearance_factor)
            #峭度指标
            Kurtosis_factor = Kurtosis_value / np.power(root_mean_score, 4)
            li.append(Kurtosis_factor)
            return li
        
        def get_all_featrue(self, n=315):
            print('-------start output all feature------')
            all_feature = [self.get_file_feature(i=i, column_num=0) for i in range(1, n+1)]
            all_feature = np.array(all_feature).reshape(-1, 10)
            all_feature = pd.DataFrame(all_feature)
            
            filename = r'D:\ml_datasets\PHM\c6_new_train\X_feature_denoise.csv'
            all_feature.to_csv(filename, index=False)
            print('-------output all feature done------')
                
                
        def plot(self,column_nu=3, show=True, x_name='X axies', y_name='y axies', i=1): #n 表示对那个 csv 文件进行可视化
            data = self.get_data(i)
            print('data shape is :' + str(data.shape))
            plt.plot(data.iloc[180000:190000, column_nu])
    #     plt.title(column_name)
            plt.xlabel(x_name, fontproperties='FangSong')
            plt.ylabel(y_name, fontproperties='FangSong')
            if show:
                plt.show()
        
        def plot_wear_amount(self, filename=None):
            data = pd.read_csv(filename)
            plt.figure(num='plot_wear_amount', figsize=((6,3)))
            
            ax = plt.gca()      #设置原点
            ax.spines['right'].set_color('none') 
            ax.spines['top'].set_color('none')
            ax.xaxis.set_ticks_position('bottom')   #将底部和左边的框设置为X， y轴
            ax.yaxis.set_ticks_position('left')
            ax.spines['bottom'].set_position(('data', 0)) #将x， y的0点，设值为原点
            ax.spines['left'].set_position(('data', 0))
            
            X = np.arange(0, 315)
            y1 = data['flute_1']
            y2 = data['flute_2']
            y3 = data['flute_3']
            y4 = (y1+y2+y3) / 3
    #         plt.plot(X, y1)
    #         plt.plot(X, y2)
    #         plt.plot(X, y3)
            plt.plot(X, y4, color='black')
            plt.plot([20, 20], [0, y4[20]], color='red', linestyle='--')
            plt.plot([180, 180], [0, y4[180]], color='blue', linestyle='--')
            plt.xticks([0, 20, 50, 100, 150, 180, 250, 300])
            plt.yticks([50, 100, 150, 200])
            plt.xlabel('走刀次数 n', fontproperties='FangSong')
            plt.ylabel(r'平均磨损量$\mu m$', fontproperties='FangSong')
            plt.text(5, 20, 'A', fontdict={'size':10, 'color':'red'})
            plt.text(100, 20, 'B', fontdict={'size':10, 'color':'blue'})
            plt.text(250, 20, 'C', fontdict={'size':10, 'color':'black'})
            plt.text(22, 65, '(20, 78.1)', fontdict={'size':10, 'color':'black'})
            plt.text(182, 110, '(180, 123.3)', fontdict={'size':10, 'color':'black'})
            plt.text(20, 85, 'a', fontdict={'size':10, 'color':'black'})
            plt.text(180, 128, 'b', fontdict={'size':10, 'color':'black'})
            print(str(y4[20])+ " ; " + str(y4[180]))
            
    #         plt.legend(['average'], loc=4)
            plt.show()
            
        def test_current_actual_commad(self, k=1):   # 未使用
    #     plt.figure(num=num, figsize=((12,6)))
            plt.subplot(2,4,1)
            self.plot('X1_CurrentFeedback',False,  '', 'X1_current (A)',i=k)
            plt.subplot(2,4,5)
            self.plot('X1_OutputCurrent',False,  'time (ms)', 'X1_output (A)',i=k)
            plt.subplot(2,4,2)
            self.plot('Y1_CurrentFeedback',False,  '', 'Y1_current (A)',i=k)
            plt.subplot(2,4,6)
            self.plot('Y1_OutputCurrent',False,  'time (ms)', 'Y1_output (A)',i=k)
            plt.subplot(2,4,3)
            self.plot('Z1_CurrentFeedback',False,  'time (ms)', 'Z1_cur (A)',i=k)
            plt.subplot(2,4,7)
            self.plot('Z1_OutputCurrent',False,  'time (ms)', 'Z1_output (A)',i=k)
            plt.subplot(2,4,4)
            self.plot('S1_CurrentFeedback',False,  'time (ms)', 'S1_cur (A)',i=k)
            plt.subplot(2,4,8)
            self.plot('S1_OutputCurrent',False,  'time (ms)', 'S1_output (A)',i=k)
        
        def test_normal_picture(self, li=[5, 170, 300], column=4):   # 画出初期、正常、急剧磨损的X轴、Y轴振动信号
            plt.rcParams['font.sans-serif'] = ['SimHei']    #显示黑体，或者： plt.xlabel('显示中文', fontproperties='SimHei')
            plt.rcParams['axes.unicode_minus'] = False
            fig, axes = plt.subplots(3, 1, figsize=(15, 8))
            X1 = self.get_data(li[0]).iloc[:, column]
            X2 = self.get_data(li[1]).iloc[:, column]
            X3 = self.get_data(li[2]).iloc[:, column]
            
            print('axes_1: max is %.2f, min is %.2f'%(np.max(X1), np.min(X1)))  # max is 0.34, min is -0.42
            print('axes_2: max is %.2f, min is %.2f'%(np.max(X2), np.min(X2)))  # max is 0.86, min is -1.22
            print('axes_3: max is %.2f, min is %.2f'%(np.max(X3), np.min(X3)))  # max is 2.02, min is -1.63
            
            axes[0].plot(X1, label='Y方向振动信号')
            axes[0].set_ylim([-0.5, 0.5])
            y_min = np.full(shape=(230000, 1), fill_value=np.min(X1))
            y_max = np.full(shape=(230000, 1), fill_value=np.max(X1))
            axes[0].plot( y_min, label='最小值= %.2f'%(np.min(X1)), linestyle='--')
            axes[0].plot(y_max, label='最大值= %.2f'%(np.max(X1)), linestyle='-.')
            axes[0].spines['right'].set_color('none') 
            axes[0].spines['top'].set_color('none')
            axes[0].set_yticks([-0.50, -0.25, 0, 0.25, 0.50])
            axes[0].legend(loc=4)
            
            axes[1].plot(X2, label='Y方向振动信号')
            axes[1].set_ylim([-1.8, 1.8])
            y_min = np.full(shape=(230000, 1), fill_value=np.min(X2))
            y_max = np.full(shape=(230000, 1), fill_value=np.max(X2))
            axes[1].plot( y_min, label='最小值= %.2f'%(np.min(X2)), linestyle='--')
            axes[1].plot(y_max, label='最大值= %.2f'%(np.max(X2)), linestyle='-.')
            axes[1].spines['right'].set_color('none') 
            axes[1].spines['top'].set_color('none')
            axes[1].set_yticks([-1.80, -0.90, 0, 0.90, 1.80])
            axes[1].legend(loc=4)
            
            axes[2].plot(X3, label='Y方向振动信号')
            axes[2].set_ylim([-3.0, 3.0])
            y_min = np.full(shape=(230000, 1), fill_value=np.min(X3))
            y_max = np.full(shape=(230000, 1), fill_value=np.max(X3))
            axes[2].plot( y_min, label='最小值= %.2f'%(np.min(X3)), linestyle='--')
            axes[2].plot(y_max, label='最大值= %.2f'%(np.max(X3)), linestyle='-.')
            axes[2].spines['right'].set_color('none') 
            axes[2].spines['top'].set_color('none')
            axes[2].set_yticks([-3.00, -1.50, 0, 1.50, 3.00])
            axes[2].legend(loc=4)
            
            axes[2].set_xlabel('采样点数', fontproperties='SimHei', fontsize=20)
            axes[1].set_ylabel(r'Y 方向加速度/$mm* s^2$', fontproperties='SimHei', fontsize=20)
            
            plt.show()
        
        def save_fig(self, n=2, current=False, acceleration=False):
            if current:
                for k in range(1, n+1):
                    data = self.get_data(i = k)    #获取对应的文件
                    name = 'E:\\hello_world'
                    name = name + str(k) + '.png'
                    print(name)
                    
                    plt.figure(num=name, figsize=((12,6)))
                    self.test_current_actual_commad(k=k)
                    plt.savefig(name)
            if acceleration:
                for k in range(1, n+1):
                    data = self.get_data(i = k)    #获取对应的文件
                    name = 'E:\\E:\\hello_world'
                    name = name + str(k) + '.png'
                    print(name)
                    
                    plt.figure(num=name, figsize=((12,6)))
                    self.test_acceleration_actual_commad(k=k)
                    plt.savefig(name)
        #将时域、时频域获取的特征， 与输出特征进行结合
        def merge_csv(self, filename = r'D:\\ml_datasets\PHM\c6_new_train\xy_final_data.csv'):
            data = pd.read_csv(filename)
            data_wear = np.empty((315, 1))
            data_wear[:21] = 0
            data_wear[21:181] = 1
            data_wear[181:] = 2
            data_wear = pd.DataFrame(data_wear, columns=['out'])
            real_data = pd.concat((data, data_wear), axis=1)
            new_filename = r'D:\\ml_datasets\PHM\c6_new_train\xy_train_data.csv'
            real_data.to_csv(new_filename, index=False)
            print('---merge done---') 
        
        def merge_xy(self):
            x_filename = r'D:\ml_datasets\PHM\c6_new_train\X_feature_denoise.csv'
            y_filename = r'D:\ml_datasets\PHM\c6_new_train\Y_feature_denoise.csv'
            x_data = pd.read_csv(x_filename)
            y_data = pd.read_csv(y_filename)
            data = pd.concat((x_data, y_data), axis=1)
            filename = r'D:\\ml_datasets\PHM\c6_new_train\xy_denoise_merge_data.csv'
            data.to_csv(filename)
            
    
    show = Plot_image(path) 
    # s = show.get_all_file()
    # print(s)
    # show.test_normal_picture()
    
    # show.merge_csv()
    # show.plot(column_nu=3, show=True, x_name='x_name', y_name='y_name', i=1)
    
    # show.get_shape(315)
    # show.get_max_min_shape(315)
    # max : 225119, min: 192314, mean: 219488
    # max : 225119, min: 192314, mean: 219488 #使用np.empty()
    # -------output done------
    # spending time : 106.582
    
    # max : 225119, min: 192314, mean: 219488 使用[] 可以减少时间
    # -------output done------
    # spending time : 95.607
    
    # show.get_shape_num(315)    #计算总的shape数
    # -------start output shape------
    # all shape rows sum is : 69138724
    # spending time : 108.445
    # 花费的时间: 108.446
    
    file1 = r'D:\ml_datasets\PHM\c1_wear.csv'
    file6 = r'D:\ml_datasets\PHM\c6_wear.csv'
    
    # show.plot_wear_amount(file)    #画磨损量的图
    # show.plot_wear_amount(file6)
    
    # show.get_all_featrue()
    
    # show.get_all_featrue() # 增加y轴振动信号的10 个特征向量
    # show.merge_xy()
    # show.merge_csv()
    
    # show.test_normal_picture()
    
    #降低样本数量，进行特征提取,选用10 万个数据量
    # show.get_all_featrue()
    # show.merge_xy()
    # show.merge_csv()
    
    
    print(show)
    
    # data = np.empty((3, 1), dtype='int32')
    # print(data)
    # for i in range(0,3):
    #     data[i] = i+1 
    # print(data)

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70 3]: /images/20220504/d47ddc3e41224fcfb0b9c837cd877dce.png