Gearbox Fault Identification Under Non-Gaussian Noise and Time-Varying Operating Conditions

2021 ◽  
pp. 1-9
Author(s):  
Stephan Schmidt ◽  
Fakher Chaari ◽  
Radoslaw Zimroz ◽  
P. Stephan Heyns ◽  
Mohamed Haddar
Keyword(s):  
Gaussian Noise ◽  
Operating Conditions ◽  
Fault Identification ◽  
Time Varying ◽  
Non Gaussian

scholarly journals A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2115 ◽  
Author(s):  
Stephan Schmidt ◽  
Radoslaw Zimroz ◽  
Fakher Chaari ◽  
P. Stephan Heyns ◽  
Mohamed Haddar
Keyword(s):  
Impulsive Noise ◽  
Operating Conditions ◽  
Fault Identification ◽  
Time Varying ◽  
Monitoring Method ◽  
Condition Indicators ◽  
Diagnosis And Prognosis ◽  
Conventional Condition ◽  
Non Gaussian ◽  
Effective Diagnosis

Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components.

scholarly journals Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

2020 ◽  
Author(s):  
Yu Wang ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  
Fumiyuki Adachi
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Time Varying ◽  
Signal Modulation ◽  
Generalization Capability ◽  
Common Features ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
Non Gaussian

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

scholarly journals Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

2020 ◽  
Author(s):  
Yu Wang ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  
Fumiyuki Adachi
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Time Varying ◽  
Signal Modulation ◽  
Generalization Capability ◽  
Common Features ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
Non Gaussian

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

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