Hydro-generators fault diagnosis with short-time-wavelet-entropy and variational auto-encoder

The prognosis and health management (PHM) of hydroelectric plants are full of difficulties caused by the complexity of the hydro-generators where each machine is different and almost unique. At industrial level, several tools are used to monitor the generator condition. Among these tools, the measurement of magnetic stray flux is one which is gaining interest. This measurement is generally based on an inductive sensor and mainly mounted near the stator. The main advantages of the magnetic stray flux are the non-invasive nature and the simplicity of its implementation. In this work, the discrete wavelet transform (DWT) is used to decompose the stray flux signal. Short-Time-Wavelet-Entropy (STWE) is then applied to extract the features from the sub-bands. Finally, a variational auto-encoder (VAE) is used in an unsupervised learning process to structure the STWE signatures of more than 400 stray flux measurement collected on real hydroelectric plants. The obtained results show that the VAE has well captured the features from the wavelet entropy (WE) signatures. An analysis of the resulting latent space shows a strong correlation between a given trajectory in the reduced space and an increase of the WE.

Evolution of Entropy in Art Painting Based on the Wavelet Transform

Quantitative studies of art and aesthetics are representative of interdisciplinary research. In this work, we conducted a large-scale quantitative study of 36,000 paintings covering both Eastern and Western paintings. The information entropy and wavelet entropy of the images were calculated based on their complexity and energy. Wavelet energy entropy is a feature that can characterize rich information in images, and this is the first study to introduce this feature into aesthetic analysis of art paintings. This study shows that the process of entropy change coincides with the development process of art painting. Further, the experimental results demonstrate an important change in the evolution of art painting, and since the rise of modern art in the twentieth century, the entropy values in painting have started to become diverse. In comparison with Western paintings, Eastern paintings have distinct low entropy characteristics in which the wavelet entropy feature of the images has better results in the machine learning classification task of Eastern and Western paintings (i.e., the F1 score can reach 97%). Our study can be the basis for future quantitative analysis and comparative research in the context of Western and Eastern art aesthetics.

Information content in the rainfall observed by INSAT-3D and IMERG: An intercomparison study over the north eastern region of India

<p>There has been a noticeable increase in the application of artificial intelligence (AI) algorithms in various areas, in the recent past. One such area is the prediction of rainfall over a region. This application has seen crucial advancement with the use of deep sequential learning algorithms. This new approach to rainfall prediction has also helped increase the utilization of satellite data for prediction. As, AI based prediction algorithms are based on data, the characteristics of it dominates the accuracy of the prediction. And one such characteristic is the information content in the data being used. This information content is classified into redundant information (information of past states in the current state) and new information. The performance of the AI based rainfall prediction depends on the amount of redundant information present in the data being used for training the AI model, more the redundant information (less the new information content) more accurate will be the prediction. Various entropy based measure have been used to quantify the new information content in the data, like permutation entropy, sample entropy, wavelet entropy, etc. This study uses a new measure called the Wavelet Entropy Energy Measure (WEEM). One of the advantages of WEEM is that it considers the dynamics of the process spread across different time scales, which other information measures have not considered explicitly. Since, the dynamics of rainfall is multi-scalar in nature, WEEM is a suitable measure for it. The main goal of this study is to find out the amount of information being generated by INSAT-3D and IMERG rainfall at each time step over the North Eastern Region of India, which will dictate the suitability of the two rainfall product to be used for AI based rainfall prediction.</p>

Interlayer Debonding Inspection in Airport Pavements by MIMO GPR System

<p>The integrity and flatness of airport pavement facilities are important to maintain safe operations of aircrafts. Even a small defect and resulting debris can cause catastrophic accidents and, therefore, anomalies must be accurately detected for the first time before major damage occurs. To this effect, it is necessary to develop a low-cost, efficient, and accurate inspection technology to detect the anomalies in airport concrete pavements. In recent years, non-destructive testing (NDT) methods have been widely used in airport pavement inspection and maintenance due to the provision of reliable and efficient information. Amongst the NDT techniques, GPR can provide optimal resolutions for different applications in civil engineering due to the ultra-wide frequency band configuration [1][2]. However, for the investigation of airport pavement facilities main challenges are how to extract information from the reflections by small anomalies [3][4].</p><p>In this research, we used a MIMO GPR system to inspect the interlayer debonding in a large area of an airport pavement. A special set of antenna arrangements of the system can obtain common mid-point (CMP) gathers during a common offset survey simultaneously. The existence of interlayer debonding affects the phase of the reflection signals, and the phase disturbance can be quantified by wavelet transform. Therefore, an advanced approach that uses the average entropy of the wavelet transform parameters in a CMP gathers to detect the interlayer debonding in airport pavements is proposed.</p><p>The aim of this research is to provide more significant and accurate information for airport pavement inspections using a MIMO GPR system. To this extent, the wavelet entropy analysis is applied to identify the interlayer debonding existed in the shallow region. The proposed approach was then evaluated by field tests on an airport taxiway. The results were validated by on-site coring and demonstrate that the regions with high entropy correspond to the regions where tiny voids occurred. The proposed method has proven potential to detect the interlayer debonding of the pavement model accurately and efficiently.</p><p> </p><p>References</p><p>[1] Alani, A. M. et al., 2020. Reverse-Time Migration for Evaluating the Internal Structure of Tree-Trunks Using Ground-Penetrating Radar. NDT&E International, vol.115, pp:102294.</p><p>[2] Zou, L. et al., 2020. Mapping and Assessment of Tree Roots using Ground Penetrating Radar with Low-Cost GPS. Remote Sensing, vol.12, no.8, pp:1300.</p><p>[3] Zou, L. et al., 2020. On the Use of Lateral Wave for the Interlayer Debonding Detecting in an Asphalt Airport Pavement Using a Multistatic GPR System. IEEE Transaction on Geoscience and Remote Sensing, vol. 58, no. 6, pp. 4215-4224.</p><p>[4] Zou, L. et al., 2021. Study on Wavelet Entropy for Airport Pavement Debonded Layer Inspection by using a Multi-Static GPR System. Geophysics, in press.</p>