Nondestructive Detection of Ceramic Products Based on Tapping Sound Signal Feature Recogition

Recent studies on the test of ceramic non-destructive testing are mainly based on high cost technologies, image processing and so on, these method possesses some drawback of low efficiency, high cost and so on. What’s more, detecting whether the ceramic products by human through listening to sound of tapping is also effectless. This paper proposed a non-destructive method for ceramic products to solve this problem. This non-destructive method consists of a tapping device and a signal processing module. The tapping device will be applied to generate the tapping sound signal and the signal processing system will be applied to analysis signal. After the process of signal analysis, sample length and peak of spectrum 2 parameters is extracted, then use these parameters to train SVM, the results will be compared with BP neural network (BPNN). The result of experiment shows that SVM with different kernels of linear, poly, rbf, sigmoid respectively reach the accuracy of 96.29%, 96.29%, 46.29%, 93.82%, while BPNN reaches the accuracy of 93.21%. This result proves that SVM can effectively complete the task of identifying defective ceramics, and its performance is better than BPNN.

A two-step vibration-sound signal fusion method for weak fault feature detection in rolling bearing systems

The vibration and sound signals get widely applications in fault diagnosis of rolling bearing systems, but the detection accuracy is unstable at different measuring positions. This paper puts forward a two-step vibration-sound signal fusion method, in which sound signal fusion and vibration-sound signal fusion are executed respectively. The sound signals are fused through weighting to the vibration signal to reduce the influence by measuring positions, and the phase difference is eliminated by a sliding window on the time axis. Then a second fusion between the vibration signal and sound signal is conducted after normalization and superposition, and the performance of two-step fusion is compared with the existing direct fusion. Results show that the two-step fusion provides a larger signal-to-noise ratio, and the amplitudes of characteristic frequencies are also higher. A cascaded bistable stochastic resonance system is applied in the post-processing of the fusion signal to make the signal features more clear, and it is proved that the fault detection effect has an obvious improvement after the whole process. This method provides a new approach for weak fault feature detection in vibration and sound signals, and is of great significance for the maintenance of rolling bearing systems.

Optimization of Intelligent Tuning System for Stringed Instruments Based on Wireless Sensor Network

With the increasing abundance of material civilization, people’s pursuit of spiritual civilization is also higher and higher. Although tuning is the analysis and processing of sound signal, the string signal is also a kind of sound signal, which follows the basic law of acoustics. In the field of wireless sensor network, this paper introduces the status quo of tuning tools for stringed instruments, analyses the existing problems of tuning tools for mainstream stringed instruments, and puts forward an automatic tuning system based on wireless sensor network. The hardware design of intelligent tuning system based on wireless sensor network includes audio acquisition module, tuning control module driver circuit design and voice broadcast module, and network communication module selection analysis. The software design of intelligent tuning system based on wireless sensor network includes embedded software design and mobile software design. Embedded software design mainly includes system flow design and automatic tuning function design. In this paper, an improved endpoint detection algorithm is used to separate the chord tone from the nonchord tone, and an improved pitch detection algorithm is used to get the accurate pitch frequency of the chord tone signal. The function and performance of the tuning system are tested to verify the reliability of the optimized intelligent tuning system.

Research on Endpoint Detection Algorithm in High Altitude Explosion Point Location Technology

Aiming at the problem of inadequate positioning accuracy of sound endpoints by the dual-element single-threshold endpoint detection algorithm and the single-element dual-threshold endpoint detection algorithm in the process of locating the sound source of weather modification bombs at high altitudes during artificial weather modification, a multi-element dual threshold endpoint detection algorithm. First, according to the characteristics of high-altitude explosion of weather modification bombs and ground reception, the sound signal is filtered and denoised, divided into frames, and windowed. Then, the time-domain feature short-term energy, short-term zero-crossing rate and frequency domain feature short-term information entropy of each frame of the sound signal are calculated, and double thresholds are set for detection. In this way, the start and end points of the explosion sound in the collected sound signal are found, and the data is imported into the positioning algorithm for processing, and then the explosion point of the weather modification bombs in the high air is located. The test results show that the method can accurately distinguish the end point of effective explosion sound, and has practical application value for the location of the sound source of the high-altitude explosion point of the weather modification bombs.