正常工作频率的英文

• normal working frequency

• 正常    normal; regular; average
• 工作频率    frequency of operation; oper ...
• 工作频率    frequency of okperation; frequency of operation; frequency of working; operating frequency; operation frequency; operational frequency; service frequency; working band; working frequency
• 不正常工作    irregular working
• 正常工作    normal operate; normal operation; normal work; proper functioning; regular work
• 正常工作点    normal working point
• 正常工作日    regular working day
• 正常工作时    normal working hour
• 最大工作频率,最高工作频率    maximum operation frequency
• 工作频率变换    operating frequency change
• 工作频率范围    operating frequency range
• 开关工作频率    carry frequency
• 声呐工作频率    sonar operating frequency
• 声纳工作频率    sonar operating frequency
• 实际工作频率    actual operating frequency
• 信道工作频率    channel rate
• 最大工作频率    maximum operation frequency
• 最低工作频率    lowest operating frequency; minimum operating frequency
• 最高工作频率    maximum operating frequency; maximum operation frequency
• 最佳工作频率    frequency optimum traffic; optimum operating frequency; optimum traffic frequency; optimum working frequence，owf; optimum working frequency （owf）; owf
• 正常工作灯头    service lamp
• 正常工作电流    running current
• 正常工作电压    normal working voltage
• 正常工作方式    normal operation mode
• 正常工作开支    normal operating expenses

• Then the difference between the output of the trained neural networks and that of the actual system is used as a residual , which detects the sensor fault . the diagnosis can then be performed by further exploiting the information provided from the residual signals according to certain rules introduced in detail in the thesis . simulating results show that the method can isolate any fault of the sensors in any input types and normal work frequencies
  仿真结果表明，基于神经网络观测器的故障诊断方法在系统正常工作频率范围之内，不受系统输入类型和故障的类型的影响，只要故障对系统的特性发生影响(系统参数漂移3 ) ，通过神经网络观测器的残差判别函数可以诊断出故障。
• First , the system operation conditions that include three input types ( square , sinuous and triangle ) with different frequencies ( l / 250hz , l / 5hz and 1hz ) are discussed . in fault detection processes , we construct 4 three - layer bp neural networks to learn the normal states of the system . the outputs of trained neural networks are as the estimate values of the sensors
  本文详细研究了在这种情况下的故障诊断问题，针对某液压系统，分析了系统在方波输入、正弦输入、锯齿波输入下，分别在正常工作频率、极限工作频率以及接近极限工作频率等9种工作状态的特性，以及在这9种工作状态下，运用神经网络观测器进行传感器故障诊断研究。