Fast multiline spectral reshaping algorithm for active vibration control

Abstract

Multiline spectral vibration (or noise) is very common in rotating machinery like motor, gearbox, compressor, propeller, etc. Active reshaping of these vibrations is one of the most important branches for active vibration control, which may find application in fields of sound quality control, psychoacoustics, military camouflage, etc. The traditional filteredreference LMS is the most popular structure for multiline spectral reshaping control. However, with the increase of spectral lines, vibration sources and observation points (feedback channels), the traditional structure will have extremely heavy computational complexity, especially for the system whose dynamic of secondary path is complex, since it requires two long filters for every single spectral line and secondary path to adjust the reference signals. In this study, we propose a fast-multiline spectrum reshaping algorithm for active vibration control. We first construct an equivalent system of the traditional multiline spectrum reshaping algorithm by introducing two extra control branches, which is able to improve the convergence property of the system. Then, we generate a group of pre-phase-scheduled reference signals using the phase information at certain frequencies of the secondary paths instead of using filters. Afterwards, we conduct a theoretical analysis of computational complexity of multiline spectrum reshaping and fast multiline spectrum reshaping algorithms. Finally, we carried out two case studies based on the FEMilS test-bed to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm performs an accurate residual vibration control with uniformed convergence speed and reduced computational complexity.

Detection technology application based on spectral subtraction and vibro acoustic principle in the measurement of ship reliability level

“…As iterations of the system increased, the fitness of all algorithms showed an increase in varying degrees, accompanied by the upward and downward fluctuation of the curve. When the iteration runs to 150 times, SS, IWT and literature (Liu et al, 2021b) do not reach a stable change trend. When the iteration runs to 91 times, the maximum fitness value of the research method is 97.2, and then it keeps stable operation.…”

“…After the optimization of the reliability level measurement system for ship parts detection, it is necessary to compare the performance of the research algorithm in the actual operation process to test its effectiveness. To ensure fairness and rationality of overall process, Spectral Subtraction (Spectral Subtraction, SS), the Improved Wavelet Transform (IWT), and the methods and research methods in literature (Liu et al, 2021b) were selected for performance comparison. Except for the special experimental parameters, the experimental parameters of all other algorithms are the same as those of the research method.…”

“…During the operation of the system, the fitness values of all algorithms change rapidly and tend to be stable within 100 generations of iteration. When the iteration runs 15 times, the fitness value of the research method starts to approach 99.9; When the operation times are 105, 110 and 89 respectively, the fitness values of IWT, literature (Liu et al, 2021b) and SS methods begin to tend to be stable, which are less than the research methods. In summary, the research method has the highest fitness value and faster convergence.…”

“…The comparison of PR curves in Figure 7 shows that when the accuracy rate of the algorithm is 0.900, the recall rate of the corresponding research method is 0.712; The recall rates of SS, IWT and literature (Liu et al, 2021b) were 0.532, 0.585, and 0.632, respectively. On the contrary, when the recall rate of all models is 0.800, the accuracy rate of the corresponding research method is 0.821; The accuracy rates of SS, IWT and literature (Liu et al, 2021b) were 0.667, 0.698, and 0.724, respectively. The results above show that when the accuracy rate or recall rate is the same value, recall rate and accuracy rate of the research method are the largest.…”

“…At the same time, ship manufacturing is inseparable from tens of thousands of parts, and its reliability must be reflected in the quality of the parts. However, in the parts that make up the ship, manufacturing defects such as inclusions, shrinkage holes and cracks are still difficult to avoid, which can easily have serious adverse effects on the safety of the ship (Liu et al, 2021a). Therefore, how to accurately evaluate the reliability level of ship manufacturing parts and promptly discover potential safety hazards has become an urgent problem in the shipping field.…”