Nibaldo Rodriguez

Bearing fault diagnosis methods play an important role in rotating machine health monitoring. In recent years, various intelligent fault diagnosis methods have been proposed, which are mainly based on the features extraction method combined with either shallow or deep learning methods. During the last few years, Shannon entropy features have been widely used in machine health monitoring, improving the accuracy of the bearing fault diagnosis process. Therefore, in this paper, we consider the combination of multi-scale stationary wavelet packet analysis with the Fourier amplitude spectrum to obtain a new discriminative Shannon entropy feature that we call stationary wavelet packet Fourier entropy (SWPFE). Features extracted by our SWPFE method are then passed onto a shallow kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. The proposed method was applied on two experimental vibration signal databases of a rolling element bearing and compared to two recently proposed methods called stationary wavelet packet permutation entropy (SWPPE) and stationary wavelet packet dispersion entropy (SWPPE). Based on our results, we can say that the proposed method is able to achieve better accuracy levels than both the SWPPE and SWPDE methods using fewer failure features. Further, as our method does not require any hyperparameter calibration step, it is less dependent on user experience/expertise.

Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. Therefore, in this paper, we propose multi-scale wavelet Shannon entropy as a discriminative fault feature to improve the diagnosis accuracy of bearing fault under variable work conditions. To compute the multi-scale wavelet entropy, we consider integrating stationary wavelet packet transform with both dispersion (SWPDE) and permutation (SWPPE) entropies. The multi-scale entropy features extracted by our proposed methods are then passed on to the kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. In the end, both the SWPDE–KELM and the SWPPE–KELM methods are evaluated on two bearing vibration signal databases. We compare these two feature extraction methods to a recently proposed method called stationary wavelet packet singular value entropy (SWPSVE). Based on our results, we can say that the diagnosis accuracy obtained by the SWPDE–KELM method is slightly better than the SWPPE–KELM method and they both significantly outperform the SWPSVE–KELM method.

The behavioural diagnostics of bearings play an essential role in the management of several rotation machine systems. However, current diagnostic methods do not deliver satisfactory results with respect to failures in variable speed rotational phenomena. In this paper, we consider the Shannon entropy as an important fault signature pattern. To compute the entropy, we propose combining stationary wavelet transform and singular value decomposition. The resulting feature extraction method, that we call stationary wavelet singular entropy (SWSE), aims to improve the accuracy of the diagnostics of bearing failure by finding a small number of high-quality fault signature patterns. The features extracted by the SWSE are then passed on to a kernel extreme learning machine (KELM) classifier. The proposed SWSE-KELM algorithm is evaluated using two bearing vibration signal databases obtained from Case Western Reserve University. We compare our SWSE feature extraction method to other well-known methods in the literature such as stationary wavelet packet singular entropy (SWPSE) and decimated wavelet packet singular entropy (DWPSE). The experimental results show that the SWSE-KELM consistently outperforms both the SWPSE-KELM and DWPSE-KELM methods. Further, our SWSE method requires fewer features than the other two evaluated methods, which makes our SWSE-KELM algorithm simpler and faster.

This paper discusses the development and evaluation of an estimation model of manufacturing costs of piping elements through the application of a Reduced Multivariate Polynomial (RMP). The model allows obtaining accurate estimations, even when enough and adequate information is not available. This situation typically occurs in the early stages of the design process of industrial products. The experimental evaluations show that the approach is capable, with a low complexity, of reducing uncertainties and to predict costs with significant precision. Comparisons with a neural network showed also that the RMP performs better considering a set of classical performance measures with the corresponding lower complexity and higher accuracy.