An Online Degradation Feature Extraction Technique for Shore Bridge Gearbox Based on Morphological Fractal Dimension and Sliding Window Weibull Fitting
Abstract:Shore bridge and other port cranes have some working condition characters including high speed, heavy load, and large impact. In order to solve the degradation feature extraction issue of hoisting mechanism gearbox, an online degradation feature extraction technique based on morphological fractal dimension and sliding window Weibull fitting is proposed. Firstly, taking the vibration energy spectrum collecting from the gearbox as the online data source, the fractal dimension of the vibration energy spectrum dur… Show more
“… Figure 4 Vibration sensors. Reproduced from Sun et al 26 . The full lifetime data are derived from the vertical vibration sensor of the high-speed input shaft of the hoisting gearbox is shown in this figure.…”
Section: Instance Data Analysismentioning
confidence: 99%
“…The signal sampling frequency was 24 kHz, the sampling time was 1 s, and the sampling interval was set as 10 s. Figure 5 Realistic diagram of a roller bearing fault. Reproduced from Sun et al 26 . When some faults occurred on the gearbox, the fault location was found at the high-speed input shaft roller bearing of the gearbox, and the failure mode was roller wear, the figure is shown in this figure.…”
Due to the doping of considerable noise and impact components in vibration signals of quay crane gearboxes, some traditional methods have difficulty uncovering degradation patterns. To accurately extract degradation features from vibration monitoring signals, a degradation feature extraction technique based on static divided symbol sequence entropy is proposed. Based on the basic scale entropy technique, considering the uniformity of the symbolization standard, the technique takes the root mean square of the health condition signal as the basis and incorporates the scale coefficient to establish a uniform basic scale. Simultaneously, the symbol set is expanded to enhance the information content and the ability of the approach to characterize the complexity levels of signals in large-value regions. Therefore, the proposed static divided symbol sequence entropy technique can accurately and flexibly characterize performance degradations. The logistic chaotic sequence and the lifetime signal of hoisting mechanism gearbox are separately used for analysis. It shows that the proposed technique can characterize the complexity of the nonlinear time series and sensitively describe the performance degradation exhibited by hoisting mechanism gearbox. This technique is computationally fast, and it can be implemented as a foundation for developing new methods for evaluating the health conditions of quay crane gearboxes.
“… Figure 4 Vibration sensors. Reproduced from Sun et al 26 . The full lifetime data are derived from the vertical vibration sensor of the high-speed input shaft of the hoisting gearbox is shown in this figure.…”
Section: Instance Data Analysismentioning
confidence: 99%
“…The signal sampling frequency was 24 kHz, the sampling time was 1 s, and the sampling interval was set as 10 s. Figure 5 Realistic diagram of a roller bearing fault. Reproduced from Sun et al 26 . When some faults occurred on the gearbox, the fault location was found at the high-speed input shaft roller bearing of the gearbox, and the failure mode was roller wear, the figure is shown in this figure.…”
Due to the doping of considerable noise and impact components in vibration signals of quay crane gearboxes, some traditional methods have difficulty uncovering degradation patterns. To accurately extract degradation features from vibration monitoring signals, a degradation feature extraction technique based on static divided symbol sequence entropy is proposed. Based on the basic scale entropy technique, considering the uniformity of the symbolization standard, the technique takes the root mean square of the health condition signal as the basis and incorporates the scale coefficient to establish a uniform basic scale. Simultaneously, the symbol set is expanded to enhance the information content and the ability of the approach to characterize the complexity levels of signals in large-value regions. Therefore, the proposed static divided symbol sequence entropy technique can accurately and flexibly characterize performance degradations. The logistic chaotic sequence and the lifetime signal of hoisting mechanism gearbox are separately used for analysis. It shows that the proposed technique can characterize the complexity of the nonlinear time series and sensitively describe the performance degradation exhibited by hoisting mechanism gearbox. This technique is computationally fast, and it can be implemented as a foundation for developing new methods for evaluating the health conditions of quay crane gearboxes.
“…In nonlinear analysis methods, symbolic dynamics analysis is an effective signal analysis method. Symbolic dynamics is proposed as a theoretical foundation for studying, nowadays, the method are used in a series of fields such as network security 15,16 , biomedicine 17,18 , and mechanical engineering 19,20 . Based on symbolic dynamics, symbolic analysis methods can discretize the original signal to extract the major trend of the signal, At the same time, they can also remove the noise, further improving the analysis effect and computational efficiency.…”
Considering the energy conservation and emission reduction is a key focus of attention. Owning to its special working condition, the quay crane usually undertake an important role. As a significant machinery in hoisting mechanism, the working states for gearbox is playing a significant role in machinery’s reliable operation. For extracting degradation indicators in huge vibration signals of quay crane, this paper proposes an improved KW symbol entropy for fault degradation feature. So as to keep united for the symbols, the technique introduces the root mean square for normal states signal and take the value as the standard and then combining a parameter named symbol coefficient in forming the unified symbols scale. Besides, a parameter named symbols number is proposed to expand the section of symbols set, the information expressing ability in improved then. Based on this, considering information entropy theory, some complexity index for symbol sequence is calculated, and then, two index are calculated as IKSE and IKDE. In order to verify the accuracy of the method proposed in this paper, an example is introduced to analyze the signals in actual working conditions. The results express that the two indexes can accurately represent the degradation trend of the signals, and for the parameter stability, has a certain application value.
“…[3][4][5] Some typical indicators proposed include spectral kurtosis, 6,7 spectral L2/L1 norm, 8 kurtosis 9 etc. Considering the nonlinear and non-stationary character of mechanical vibration signals, a complexity analysis method is used based on information from entropy and fractal dimension and is usually employed in feature analysis of rotating machinery such as bearings and gears in recent years, such as modified multi-scale symbolic dynamic entropy, 10 LMDsample entropy, 11 multi scale permutation entropy, 12,13 morphological fractal dimension 14 etc. Some previous studies have shown that vibration signals of the quay crane have a certain degree of complexity character.…”
A degradation assessment technique based on an online improved symbol sequence entropy online_ISSE and a logistic regression model is proposed in this paper. Firstly, the threshold factor is introduced to retain the `coarse graining' information of direction changing and amplitude information, the `sensitivity' of improved symbol sequence entropy (SSE) to impact components is reduced and improved symbol sequence entropy (ISSE) is proposed. Then, a sliding window and Weibull distribution theory are used to effectively filter out the influence of fluctuations in the ISSE feature sequence, forming the degradation feature named online_ISSE. Finally, a logistic regression model is trained and constructed, and the health factor CV is calculated online to assess the degradation condition of the unknown signal samples. The lifetime vibration signal of the hoisting gearbox monitored from #8114 quay crane of the Shanghai Port Container Terminal is introduced for instance analysis. The results show that the proposed ISSE has a better effect in describing the complexity pattern than the SSE algorithm and that the degradation condition can be tracked and assessed accurately based on the technique proposed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
