写一下轴承故障诊断的代码
时间: 2023-03-04 13:33:16 浏览: 78
轴承故障诊断通常需要结合振动信号和声音信号等多种传感器数据进行分析,代码实现比较复杂。以下是一种简单的基于振动信号的轴承故障诊断代码示例:
```python
import numpy as np
from scipy.fftpack import fft
from scipy.signal import hilbert
# 加载振动信号数据
vibration_data = np.load('vibration_data.npy')
# 定义常量
SAMPLING_RATE = 1000 # 采样频率
N = vibration_data.shape[0] # 信号长度
FREQ_RES = SAMPLING_RATE / N # 频率分辨率
# 按时间窗口分段处理振动信号,窗口大小为1024个采样点
window_size = 1024
num_windows = int(N / window_size)
# 定义诊断指标阈值
RMS_THRESHOLD = 0.1 # RMS幅值阈值
SKEWNESS_THRESHOLD = 0.2 # 偏度阈值
KURTOSIS_THRESHOLD = 0.3 # 峰度阈值
# 定义故障类型
FAULT_TYPE_NONE = 0 # 无故障
FAULT_TYPE_OUTER_RACE = 1 # 外环故障
FAULT_TYPE_INNER_RACE = 2 # 内环故障
FAULT_TYPE_ROLLING_ELEMENT = 3 # 滚动体故障
# 循环处理每个时间窗口
for i in range(num_windows):
# 获取当前时间窗口的振动信号
window_start = i * window_size
window_end = (i + 1) * window_size
signal = vibration_data[window_start:window_end]
# 计算振动信号的RMS幅值
rms = np.sqrt(np.mean(signal ** 2))
if rms > RMS_THRESHOLD:
print('RMS幅值超过阈值,可能存在故障')
# 计算振动信号的偏度和峰度
skewness = np.mean((signal - np.mean(signal)) ** 3) / np.std(signal) ** 3
kurtosis = np.mean((signal - np.mean(signal)) ** 4) / np.std(signal) ** 4
# 根据偏度和峰度判断故障类型
fault_type = FAULT_TYPE_NONE
if skewness > SKEWNESS_THRESHOLD:
if kurtosis > KURTOSIS_THRESHOLD:
fault_type = FAULT_TYPE_ROLLING_ELEMENT
print('滚动体故障')
else:
fault_type = FAULT_TYPE_OUTER_RACE
print('外环故障')
elif skewness < -SKEWNESS_THRESHOLD:
fault_type = FAULT_TYPE_INNER_RACE
print('内环故障')
# 计算振动信号的频谱,用于分析故障特征频率
spectrum = np.abs(fft(signal))
freqs = np.linspace(0, SAMPL