Acoustical materials are usually used for architectural design, ambient noise, and traffic noise to absorb sound. The sound absorption coefficient (SAC) is often used as the performance index of acoustical materials. It is measured using the reverberation room method (RRM) and the impedance tube method (ITM). The sound source of the RRM is a random incidence in various directions, whereas that of the ITM is a normal incident. The SAC measured by the RRM is more extensive, practical, and costly than ITM. Usually, the SAC measured by RRM is used as the commercially available acoustical materials index. However, RRM requires large-area test samples for measurement and a special sound field of reverberation room for test experiments. Therefore, this study aims to use the ITM to measure SAC. The measured SAC, frequency, thickness, and material density were employed as independent variables and substituted in the obtained multiple regression model to predict SAC obtained by RRM. In addition, the measurement characteristics of impedance tubes provide varisized calibers for different frequency ranges to measure a relatively accurate SAC. Based on the abovementioned characteristics, this study proposed a dual-model for estimating the SAC of the reverberation room method to achieve the best-estimated result. After experimental validation, the dual-model estimated result was compared with SAC from the reverberation room method. It was observed that the maximum absolute value of all errors did not exceed 0.16. Most of the absolute errors were below 0.1, proving the accuracy of this method. Therefore, the method proposed in this study can shorten the acoustical material development schedule for manufacturers and save the cost of the development process.

This study investigated the sound absorption coefficient of micro-perforated panels with multiple design factors, using the Taguchi method for analysis and optimization. The panels, made using stereolithography technology for three-dimensional printing, had four key factors: perforation rate, hole diameter, panel thickness, and wave surface. Their absorption coefficients were measured using an impedance tube and analyzed in two directions: namely, the absorption coefficient across various frequency bands and the frequency corresponding to the maximum absorption coefficient. By utilizing the Taguchi method's orthogonal array, this study calculated the interaction and influence of these factors on sound absorption, completing the optimization and prediction of the experiment. Results showed that perforation rate and panel thickness significantly affected the absorption coefficient across all frequencies, while hole diameter mainly influenced low-frequency absorption. The wavy surface had a minor impact. The study optimized micro-perforated panels with the Taguchi method using less experimental data, and experimental verification was performed, which aligned with the prediction results. This study demonstrated that optimizing micro-perforated panels with the Taguchi method is efficient and accurately identifies the optimal combination, allowing predictions of absorption for the noises in different frequency bands or specific frequencies.

Fault diagnosis is an important method for maintaining the stable and safe running state of mechanical equipment. As most mechanical equipment faults are induced by the bearing assembly, bearing fault diagnosis is of considerable importance. At present, the mainstream intelligent diagnostic techniques include supervised learning and unsupervised learning. Supervised learning requires manual labeling and data classification, which is unfavorable for massive data amounts. Therefore, how to effectively use labeled data to increase the accuracy of diagnosis is critical, especially when the bearing failure cannot be labeled at the very beginning. This paper proposes a time–frequency analysis of the short-time Fourier transform and wavelet transform methods based on unsupervised learning. The time axis was integrated to obtain the marginal frequency of two frequency domains as a diagnostic feature, and then two clustering centroids were established automatically by the K-means of unsupervised learning. The signals were divided into two classes based on the nearest clustering centroid as the criteria for diagnosis. Finally, other bearings in different positions were classified and diagnosed using the nearest clustering centroid in the same experiment to verify the effectiveness of the method proposed in this study.

Bearings are one of the critical components of any mechanical equipment. They induce most equipment faults, and their health status directly impacts the overall performance of equipment. Therefore, effective bearing fault diagnosis is essential, as it helps maintain the equipment stability, increasing economic benefits through timely maintenance. Currently, most studies focus on extracting fault features, with limited attention to establishing fault thresholds. As a result, these thresholds are challenging to utilize in the automatic monitoring diagnosis of intelligent devices. This study employed the generalized fractal dimensions to effectively extract the feature of time-domain vibration signals of bearings. The optimal fault threshold model was developed using the receiver operating characteristic curve, which served as the baseline of exception judgment. The extracted fault threshold model was verified using two bearing operation experiments. The experimental results revealed different damaged positions and components observed in the two experiments. The same fault threshold model was obtained using the method proposed in this study, and it effectively diagnosed the abnormal states within the signals. This finding confirms the effectiveness of the diagnostic method proposed in this study.

The bearing is an important part of mechanical equipment. Its condition directly influences the operation of the entire mechanical equipment. Faults in bearings may induce fatal disasters and heavy economic losses, in worst-case scenarios. In fault diagnosis, many studies have only proposed the characteristics of faults; however, without standards for quantifying the faults, automated diagnosis could not be realized. Based on the generalized fractal dimensions (GFDs) and the receiver operating characteristic (ROC) curve, this study proposed a method to characterize initial failure trends and quantize the bearing failure standard. The size of GFDs was calculated using the vibration signal measured during the operation of a bearing. The optimal classification model was determined based on the ROC curve as the criterion of damage. The model trained by the signals of the training group was used in the check analysis of the signals of the validation group. The accuracy of this quantitative analysis was validated by experimental results. Finally, three bearings in different positions were diagnosed by this model under the same test conditions, and the effectiveness of the quantitative diagnosis of damage proposed in this study was validated.