Gear pitting level diagnosis using vibration signals with an improved inception structure

Gear pitting fault is common in mechanical devices. At present, most of the gear pitting fault detection methods are based on the manual extraction of the frequency domain features from vibration signals. This paper presents a method for gear pitting fault level diagnosis using vibration signals with an improved inception network. The presented method directly applies to the vibration signals to automatically extract features and diagnose the level of the gear pitting fault using deep learning. The presented method has been validated with vibration data collected for 7 gear pitting conditions from gear pitting fault tests. The validation results have shown that the presented method can effectively classify the levels of the gear pitting faults. In comparison with traditional convolutional neural network, the diagnosis accuracy has been significantly improved with the presented method

Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformation

We study the problem of few-shot physically-aware articulated mesh generation. By observing an articulated object dataset containing only a few examples, we wish to learn a model that can generate diverse meshes with high visual fidelity and physical validity. Previous mesh generative models either have difficulties in depicting a diverse data space from only a few examples or fail to ensure physical validity of their samples. Regarding the above challenges, we propose two key innovations, including 1) a hierarchical mesh deformation-based generative model based upon the divide-and-conquer philosophy to alleviate the few-shot challenge by borrowing transferrable deformation patterns from large scale rigid meshes and 2) a physics-aware deformation correction scheme to encourage physically plausible generations. We conduct extensive experiments on 6 articulated categories to demonstrate the superiority of our method in generating articulated meshes with better diversity, higher visual fidelity, and better physical validity over previous methods in the few-shot setting. Further, we validate solid contributions of our two innovations in the ablation study. Project page with code is available at https://meowuu7.github.io/few-arti-obj-gen.Comment: ICCV 2023. Project Page: https://meowuu7.github.io/few-arti-obj-ge

Integrating the impacts of vegetation coverage on ecosystem services to determine ecological restoration targets for adaptive management on the Loess Plateau, China

Achieving sustainable resource management is essential to address the rising demand for ecosystem services. The absence of targeted vegetation restoration based on ecological function positioning has, nevertheless, made it challenging to effectively combat the ecological decline. This study attempted to classify four dominant ecological function areas based on the assessment of water conservation, soil retention, habitat quality, and food supply and determined the vegetation coverage threshold by exploring the trade-offs among ecosystem services and constraint effects between ecosystem services and vegetation coverage. The results highlighted the impacts of ecosystem services on vegetation coverage across the years 1990, 2000, 2010, and 2020 and established differentiated ecological restoration targets. The optimal vegetation coverage in the water conservation area was found to be 58%–63%, in the soil retention area was 52%–56%, in the food supply area was 34%–40%, and in the habitat quality area was 65%–70%. Finally, the study identified the subwatersheds with reasonable vegetation coverage, excessive restoration, and those that failed to reach the optimal vegetation coverage to develop targeted restoration strategies for each subwatershed according to its unique vegetation conditions. This study provides valuable insights into the specification of differentiated vegetation coverage targets and serves as a useful tool for more effective ecosystem planning and management.</p

Fatigue life prognostic for medium-carbon steel based S-N curve computation and deep autoencoder

Medium-carbon steel is widely used in architecture, rail, machinable steel and so on. So, there is a huge significance in the analysis and research of its fatigue life. In this paper, the fatigue experiment with different surface roughness was set up. In the experiment, there were three type of roughness and a total of 75 experiments were performed. Then a deep autoencoder to model the relationship of roughness (R), stress (S), and fatigue life (N). It has been found that the method can automatically extract the features which can be used to effectively model the relationship of R, S and N. Finally, the approach presented was compared with the existing model based on Tanaka-Mura theory and got an unexpected better result

A Connectivity Metrics-Based Approach for the Prediction of Stress-Dependent Fracture Permeability

Rapid and accurate assessment of fracture permeability is critical for subsurface resource and energy development as well as rock engineering stability. Fracture permeability deviates from the classical cubic law under the effect of roughness, geological stress, as well as mining-induced stress. Conventional laboratory tests and numerical simulations are commonly costly and time-consuming, whereas the use of a connectivity metric based on percolation theory can quickly predict fracture permeability, but with relatively low accuracy. For this reason, we selected two static connectivity metrics with the highest and lowest prediction accuracy in previous studies, respectively, and proposed to revise and use them for fracture permeability estimation, considering the effect of isolated large-aperture regions within the fractures under increasing normal stress. Several hundred fractures with different fractal dimensions and mismatch lengths were numerically generated and deformed, and their permeability was calculated by the local cubic law (LCL). Based on the dataset, the connectivity metrics were counted using the revised approach, and the results show that, regardless of the connectivity metrics, the new model greatly improves the accuracy of permeability prediction compared to the pre-improved model, by at least 8% for different cutoff aperture thresholds

A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM

Research on data-driven fault diagnosis methods has received much attention in recent years. The deep belief network (DBN) is a commonly used deep learning method for fault diagnosis. In the past, when people used DBN to diagnose gear pitting faults, it was found that the diagnosis result was not good with continuous time domain vibration signals as direct inputs into DBN. Therefore, most researchers extracted features from time domain vibration signals as inputs into DBN. However, it is desirable to use raw vibration signals as direct inputs to achieve good fault diagnosis results. Therefore, this paper proposes a novel method by stacking spare autoencoder (SAE) and Gauss-Binary restricted Boltzmann machine (GBRBM) for early gear pitting faults diagnosis with raw vibration signals as direct inputs. The SAE layer is used to compress the raw vibration data and the GBRBM layer is used to effectively process continuous time domain vibration signals. Vibration signals of seven early gear pitting faults collected from a gear test rig are used to validate the proposed method. The validation results show that the proposed method maintains a good diagnosis performance under different working conditions and gives higher diagnosis accuracy compared to other traditional methods

Diagnostyka pittingu kół zębatych na podstawie surowego sygnału emisji akustycznej w oparciu o głębokie uczenie maszynowe

Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults.Pitting kół zębatych stanowi jedno z najczęstszych uszkodzeń przekładni mechanicznych. Do wykrywania takich uszkodzeń stosuje się sygnały emisji akustycznej (AE), które, ze względu na niższą wrażliwość na hałas otoczenia, stanowią skuteczniejsze narzędzie diagnostyczne niż tradycyjne sygnały wibracyjne. Wykrywalność zużycia guzełkowatego (pittingu) kół zębatych przy użyciu sygnałów AE i splotowych sieci neuronowych jest jednak niska. Aby rozwiązać ten problem, w niniejszym artykule zaproponowano nową metodę diagnozowania uszkodzeń kół zębatych za pomocą surowych sygnałów AE, którą nazwano augmented convolution sparse autoencoder (konwolucją rozszerzoną z wykorzystaniem autoenkodera rzadkiego, ACSAE). Jest to metoda samouczenia jednowymiarowych splotowych sieci neuronowych realizowanego za pomocą autoenkodera rzadkiego. Metoda ta wykorzystuje teorię wzmocnienia do zwiększania adaptacyjności i odporności sieci. Metoda ACSAE pozwala na automatyczne wyodrębnianie cech degradacji bezpośrednio z oryginalnych sygnałów AE bez konieczności ich konwersji do domeny czasu i częstotliwości. Walidację metody przeprowadzono na podstawie sygnałów AE otrzymanych w badaniach kół zębatych. Analiza wyników badań pittingu kół zębatych wskazuje, że proponowana metoda pozwala na skuteczną detekcję tego typu uszkodzeń, przy wskaźniku wykrywalności powyżej 98%. Analiza porównawcza pokazuje, że metoda ACSAE cechuje się większą trafnością diagnostyczną w wykrywaniu błędów montażowych kół zębatych w porównaniu z sieciami neuronowymi w pełni połączonymi, splotowymi i rekurencyjnymi

Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals

This paper deals with gear pitting fault diagnosis problem and presents a method by integrating convolutional neural network (CNN) and gated recurrent unit (GRU) networks with vibration and acoustic emission signals to solve the problem. The presented method first trains a one-dimensional CNN with acoustic emission signals and a GRU network with vibration signals. Then the gear pitting fault features obtained by the two networks are concatenated to form a deep learning structure for gear pitting fault diagnosis. Seven different gear pitting conditions are used to test the feasibility of the presented method. The diagnosis result of the gear pitting fault shows that the accuracy of the presented method reaches above 98% with only a relatively small number of training samples. In comparison with the results using CNN or GRU network alone, the presented method gives more accurate diagnosis results. By comparing the results of different loads and learning rates, the robustness of the presented method for gear pitting fault diagnosis is proved. Moreover, the presented deep structure can be easily extended to more other sensor input signals for gear pitting fault diagnosis in the future