Abstract
Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.
Highlights
At present, synthetic diamonds are produced in a high-temperature and high-pressure environment formed by electric heating and the hydraulic press in the high-press apparatus
If Min Mid is less than α(α < 1) times the minimum of two peaks (Min Mid < α·min{ Max For, Max Lat }), it is separated into two pulse sound signals; otherwise, it is regarded as a single pulse sound signal. α is called the double pulse extraction parameter
An intelligent monitoring network is proposed for monitoring the cracks of the anvils of the high-press apparatus used in synthetic diamond industry
Summary
Synthetic diamonds are produced in a high-temperature and high-pressure environment formed by electric heating and the hydraulic press in the high-press apparatus In this process, the anvil cracks may be caused by material fatigue as a result of the alternating high temperature and pressure. Shahidan uses the AE wave descriptors, including AE amplitude, rise time, and average frequency, to identify the crack patterns in concrete beams [12] This method has achieved some results, there are still lots of defects. The method proposed in this paper realizes real-time monitoring of the carbide anvils of the high-press apparatus
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