No abstract

Noble metals and Cu mainly are recycled in treating waste printed circuit boards (PCBs), and a large amount of nonmetallic materials in PCBs are disposed of by combustion or landfill, which may cause secondary pollution and resource-wasting. In this study, a kind of nonmetallic plate (NMP) has been produced by nonmetallic materials of pulverized waste PCBs. The NMP is produced by a self-made hot-press former through adding resin paste as a bonding agent. Furthermore, microshapes of nonmetallic materials and effects of the contents and particle sizes of nonmetallic materials on mechanical properties of the NMP are investigated. It has been found that the nonmetallic materials with particle size from 0.3 to 0.15 mm are in the form of fiber bundles, with the majority of fibers being encapsulated in resin. Nonmetallic materials shorter than 0.07 mm consist of single glass fiber and resin powder. When nonmetallic materials content was 20 wt %, the NMP with particle size of nonmetallic materials less than 0.07 mm, has excellent mechanical properties, which results in a flexural strength of 68.8 MPa and a Charpy impact strength of 6.4 kJ/m 2 . This novel technique offers a possibility for recycling of nonmetallic materials of PCBs and resolving the environmental pollutions during recycling of PCBs.

The planetary gearbox is at the heart of most rotating machinery. The premature failure and subsequent downtime of a planetary gearbox not only seriously affects the reliability and safety of the entire rotating machinery but also results in severe accidents and economic losses in industrial applications. It is an important and challenging task to accurately detect failures in a planetary gearbox at an early stage to ensure the safety and reliability of the mechanical transmission system. In this paper, a novel method based on wavelet packet energy (WPE) and modulation signal bispectrum (MSB) analysis is proposed for planetary gearbox early fault diagnostics. First, the vibration signal is decomposed into different time-frequency subspaces using wavelet packet decomposition (WPD). The WPE is calculated in each time-frequency subspace. Secondly, the relatively high energy vectors are selected from a WPE matrix to obtain a reconstructed signal. The reconstructed signal is then subjected to MSB analysis to obtain the fault characteristic frequency for fault diagnosis of the planetary gearbox. The validity of the proposed method is carried out through analyzing the vibration signals of the test planetary gearbox in two fault cases. One fault is a chipped sun gear tooth and the other is an inner-race fault in the planet gear bearing. The results show that the proposed method is feasible and effective for early fault diagnosis in planetary gearboxes.

Transient impulses are important information for machinery fault diagnosis. However, the transient features contained in the vibration signals generated by planetary gearboxes are usually immersed by a large amount of background noise and harmonic components. Even mathematical morphology (MM) is an excellent anti-noise signal processing method that can directly extract the geometry of impulse features in the time domain, but the four basic operators of MM can only extract one-way impulses while cannot extract the bidirectional impulses effectively at the same time. To accurately extract the impulse feature information, a novel method for fault detection of planetary gearbox based on an enhanced average (EAVG) filter and modulated signal bispectrum (MSB) is proposed. Firstly, the properties of the extracted impulses based on the four basic operators of MM will be divided into two categories of enhanced average operators. The four EAVG filters consist of the average weighted combination of enhanced average operators, and then the best EAVG filter is selected based on correlation coefficient to implement on the original vibration signal. It allows EAVG filter to extract positive and negative impulses of vibration signal, thereby improving the accuracy of planetary gearbox fault detection.Subsequently, the performance of the EAVG filter is influenced by the length of its structural element (SE), which is adaptively determined using an indicator based kurtosis. Then, the EAVG filter selects the optimal SE length to eliminate the interference of background noise and harmonic components to enhance the impulse components of the vibration signal. However, the nonlinear modulation components that are related to the fault types and severities are not extracted exactly and still remained in the filtered signal by EAVG. Finally, the MSB is utilized to the EAVG filtered signal to decompose the modulated components and extract the fault features. The advantages of EAVG over average (AVG) filter are clarified in the simulation study. In addition, the EAVG-MSB is validated by analyzing the vibration signals of planetary gearboxes with sun gear chipped tooth, sun gear misalignment and bearing inner race fault. The results indicate that the EAVG-MSB is effective and accurate in feature extraction compared with the combination morphological filter-hat transform (CMFH) and average combination difference morphological filter (ACDIF), and the feasibility of the EAVG-MSB are proved for planetary gearbox condition monitoring and fault diagnosis.

To extract impulsive feature from vibration signals with strong background noise and interference components for accurate bearing diagnostics., a multi-stage noise reduction method is proposed based on ensemble empirical mode decomposition (EEMD), wavelet denoising and modulation signal bispectrum (MSB) Firstly, noisy vibration signals are applied with EEMD to obtain a list of intrinsic mode functions (IMFs) that leverage the desired modulation components to different degrees. Then, a number of initial IMFs in the high frequency range, which are separated by using the mean of the standardized accumulated modes (MSAM) to have more modulation contents, are further denoised by a wavelet shrinkage approach. These cleaned IFMs in the high frequency are combined with the low frequency IFMs to construct a pre-denoised signal that maintains the modulation properties of the signal. Finally, a modulation signal bispectrum (MSB) is used to extract the modulation signature by suppressing further the residual random noise and deterministic interference components. This multiple stage noise reduction method is validated through a simulation study and two experimental fault cases studies of rolling element bearing. The results are more accurate and reliable in diagnosing the inner and outer race defects in comparison with the individual use of the start of the art EEMD or MSB. The MSAM is taken as a novel criterion to divide the IMFs into low-and high-frequency parts. Subsequently, a wavelet based pre-denoising is used to process the high-frequency IMFs, which is then joined with the low-frequency IMFs to generate a reconstructed signal with much less noise. The EEMD-Wavelet model can effectively reduce the background noise and enhance the impulse characteristic in the vibration signal. However, the nonlinear modulation and uncoupling frequency components are still existed in the reconstructed signal. Finally, the modulation signal bispectrum (MSB) is explored to decompose the modulated components and extract the fault-related characteristics from the reconstructed signal. The proposed method is validated through a simulation study and two experimental fault cases studies of rolling element bearing. The analysis results demonstrate that the proposed method is effective in the fault feature extraction with high accuracy in comparison with the individual EEMD and MSB.