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With the vigorous development of industrial automation and the Internet of things, the transmission of data is more dependent on immediacy, so network devices have higher and higher requirements for time synchronization accuracy. The clock source of common network devices is provided by the transistor oscillator in the server, but the oscillator will change with factors such as aging and temperature, and it cannot be guaranteed that the oscillator in the server will work at the same frequency. Time synchronization can be achieved by technologies such as IRIG-B, NTP, or IEEE 1588 (PTP), but the hardware cost of building IRIG-B is high, and NTP has the lowest cost, but it can only provide time accuracy from milliseconds to microseconds. PTP can provide sub-microsecond or even nanosecond time precision. It is a system of time synchronization mechanisms through Ethernet transmission. In this article, we first propose a time synchronization system using the development board and PDP protocol. On the Xilinx Zynq-7000 SOC platform of Petalinux, we implement the hardware solution of Linux PTP. The hardware time stamp is 20 ns. To improve the accuracy, the congenital frequency error between the oscillators must be considered. Therefore, a PTP auxiliary time stamp with dynamic frequency compensation is proposed and designed into a chip. Experimental results show that at 45 nm (TN40G) it can operate at 370 MHz and achieve 2.7 ns resolution, which can be applied to more demands. [ABSTRACT FROM AUTHOR]

The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN's learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%. [ABSTRACT FROM AUTHOR]

In the industrial Internet of Things, the network time protocol (NTP) can be used for time synchronization, allowing machines to run in sync so that machines can take critical actions within 1 ms. However, the commonly used NTP mechanism does not take into account that the network packet travel time over a link is time-varying, which causes the NTP to make incorrect synchronization decisions. Therefore, this paper proposed a low-cost modification to NTP with clock skew compensation and adaptive clock adjustment, so that the clock difference between the NTP client and NTP server can be controlled within 1 ms in the wired network environment. The adaptive clock adjustment skips the clock offset calculation when the NTP packet run trip time (RTT) exceeds a certain threshold. The clock skew compensation addresses the inherent issue that different clocks (or oscillators) naturally drift away from each other. Both adaptive clock adjustment and clock skew compensation are environment dependent and device dependent. The measurement result in our experimental environment shows that the when the RTT threshold is set at 1.7 ms, the best synchronization accuracy is achieved. [ABSTRACT FROM AUTHOR]

The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN's learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%.