Degradation state identification for hydraulic pumps using modified hierarchical decomposition and image processing

Monitoring the degradation state of hydraulic pumps is of great significance to the safe and stable operation of equipment. As an important step, feature extraction has always been challenging. The non-stationary and nonlinear characteristics of vibration signals are likely to weaken the performance of traditional features. The two-dimensional image representation of vibration signals can provide more information for feature extraction, but it is challenging to obtain sufficient information based on small-size images. To solve these problems, a method for feature extraction based on modified hierarchical decomposition (MHD) and image processing is proposed in this paper. First, a set of signals decomposed by MHD are converted into gray-scale images. Second, features from accelerated segment test (FAST) algorithm are applied to detecting the feature points of the gray-scale image. Third, the real part of Gabor filter bank is used to convolve the images, and the responses of feature points are used to calculate histograms that are regarded as feature vectors. The method for feature extraction fully acquires the multi-layered texture information of small-size images and removes the redundant information. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the proposed method are validated using experimental data, and the results show that the highest recognition rate of our proposed method can reach 100%. The results of the comparison among the proposed method, local binary pattern (LBP), and one-dimensional ternary patterns (1D-TPs) certify the superiorities of the proposed method. It obtains the highest classification accuracy (99.7%–98%) and the lowest feature set dimension (13–10).

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method’s effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend.

Rolling bearing performance degradation assessment based on singular value decomposition-sliding window linear regression and improved deep learning network in noisy environment

It is difficult to evaluate the degradation performance and the degradation state of the rolling bearing in noisy environment. A new method is proposed to solve the problem based on singular value decomposition (SVD)-sliding window linear regression and ResNeXt - multi-attention mechanism's deep neural network (RMADNN). Firstly, the root mean square(RMS) gradient value is calculated on the basis of RMS based on SVD and linear regression of sliding window, which is used as the rolling bearing performance degradation indicator in noisy environment. Secondly, the degradation state of rolling bearing is divided by the RMS gradient. Thirdly, for the part of the deep learning network model, the soft attention mechanism is introduced into the bidirectional long short-term memory network (BiLSTM) to extract more important and deep fault features. At the same time, the ResNeXt layer is added into the convolutional neural network (CNN) to extract more fault features and merge them through multi-scale grouped convolution. Then, the hybrid domain attention mechanism (HDAM) was introduced after the ResNext layer. The HDAM can screen out more important features from the output features of the ResNext in the two dimensions of channel and spatial. Therefore, the improved deep learning network of the ResNeXt - multi-attention mechanism's deep neural network (RMADNN) in this research is established. Finally, the labeled data set is input into the improved model for training, and the Softmax classifier is used to identify the life decline state of the rolling bearing. The result shows that the indicator of RMS gradient proposed has a better characterization, and the RMADNN model can distinguish the life degradation state of rolling bearing better.

Impact of the Paper Degradation State and Constituents on Its Behavior During and After X-ray Exposure

Paper is a complex biopolymer material which contains papermaking additives and often bears inks and other graphic media. Cultural heritage paper-based artefacts are most often deteriorated to some extent. This research explores how intrinsic factors such as constituents and degradation state can impact the modifications incurred in aged papers during and after X-ray examination. To this end laboratory model papers, artificially aged, and 18th and 19th century archival documents, with and without additives (gelatin, calcium carbonate) and iron gallate ink, were exposed to Synchrotron X-ray radiation at doses that were previously shown to incur damage in unaged cotton papers (0.7 to 4 kGy). Glycosidic scissions, hydroxyl free radicals, UV luminescence and yellowing were measured immediately after the irradiation, and were monitored over a period of three years. The depolymerization of cellulose was lower in the aged papers, as well as in the papers containing calcium carbonate and gelatin, than in the unaged fully cellulosic papers. Compared to the papers with no additives, there were more hydroxyl free radicals in the papers with calcium carbonate and slightly less in the gelatin sized papers. UV luminescence and yellowing both appeared post-irradiation, with a delay of several weeks to months, while the intensity of the responses was impacted by the various paper constituents. The papers with iron gallate ink showed limited degradation in the low doses range, most probably due to recombination of the free radicals produced. Doses below 4 kGy did not cause yellowing or UV luminescence of the archival papers within the whole monitoring period. At higher doses (26 to 36 kGy), a slight UV luminescence appeared after 21 months, as well as a slight yellowing after three years, in some of them. No clear correlation between the degradation induced by the irradiation and the constituents in the paper nor its conservation state could be made. The archival papers in good conservation state depolymerized to the same extent as the model papers, while the most degraded archival papers were less impacted than the latter.