Performance Analysis and Enhancement of Deep Convolutional Neural Network - Application to Gearbox Condition Monitoring

Convolutional neural network has been widely investigated for machinery condition monitoring, but its performance is highly affected by the learning of input signal representation and model structure. To address these issues, this paper presents a comprehensive deep convolutional neural network (DCNN) based condition monitoring framework to improve model performance. First, various signal representation techniques are investigated for better feature learning of the DCNN model by transforming the time series signal into different domains, such as the frequency domain, the time–frequency domain, and the reconstructed phase space. Next, the DCNN model is customized by taking into account the dimension of model, the depth of layers, and the convolutional kernel functions. The model parameters are then optimized by a mini-batch stochastic gradient descendent algorithm. Experimental studies on a gearbox test rig are utilized to evaluate the effectiveness of presented DCNN models, and the results show that the one-dimensional DCNN model with a frequency domain input outperforms the others in terms of fault classification accuracy and computational efficiency. Finally, the guidelines for choosing appropriate signal representation techniques and DCNN model structures are comprehensively discussed for machinery condition monitoring

Carvacrol, a Food-Additive, Provides Neuroprotection on Focal Cerebral Ischemia/Reperfusion Injury in Mice

Carvacrol (CAR), a naturally occurring monoterpenic phenol and food additive, has been shown to have antimicrobials, antitumor, and antidepressant-like activities. A previous study demonstrated that CAR has the ability to protect liver against ischemia/reperfusion injury in rats. In this study, we investigated the protective effects of CAR on cerebral ischemia/reperfusion injury in a middle cerebral artery occlusion mouse model. We found that CAR (50 mg/kg) significantly reduced infarct volume and improved neurological deficits after 75 min of ischemia and 24 h of reperfusion. This neuroprotection was in a dose-dependent manner. Post-treatment with CAR still provided protection on infarct volume when it was administered intraperitoneally at 2 h after reperfusion; however, intracerebroventricular post-treatment reduced infarct volume even when the mice were treated with CAR at 6 h after reperfusion. These findings indicated that CAR has an extended therapeutic window, but delivery strategies may affect the protective effects of CAR. Further, we found that CAR significantly decreased the level of cleaved caspase-3, a marker of apoptosis, suggesting the anti-apoptotic activity of CAR. Finally, our data indicated that CAR treatment increased the level of phosphorylated Akt and the neuroprotection of CAR was reversed by a PI3K inhibitor LY-294002, demonstrating the involvement of the PI3K/Akt pathway in the anti-apoptotic mechanisms of CAR. Due to its safety and wide use in the food industry, CAR is a promising agent to be translated into clinical trials

Hormone-Dependent Expression of a Steroidogenic Acute Regulatory Protein Natural Antisense Transcript in MA-10 Mouse Tumor Leydig Cells

Cholesterol transport is essential for many physiological processes, including steroidogenesis. In steroidogenic cells hormone-induced cholesterol transport is controlled by a protein complex that includes steroidogenic acute regulatory protein (StAR). Star is expressed as 3.5-, 2.8-, and 1.6-kb transcripts that differ only in their 3′-untranslated regions. Because these transcripts share the same promoter, mRNA stability may be involved in their differential regulation and expression. Recently, the identification of natural antisense transcripts (NATs) has added another level of regulation to eukaryotic gene expression. Here we identified a new NAT that is complementary to the spliced Star mRNA sequence. Using 5′ and 3′ RACE, strand-specific RT-PCR, and ribonuclease protection assays, we demonstrated that Star NAT is expressed in MA-10 Leydig cells and steroidogenic murine tissues. Furthermore, we established that human chorionic gonadotropin stimulates Star NAT expression via cAMP. Our results show that sense-antisense Star RNAs may be coordinately regulated since they are co-expressed in MA-10 cells. Overexpression of Star NAT had a differential effect on the expression of the different Star sense transcripts following cAMP stimulation. Meanwhile, the levels of StAR protein and progesterone production were downregulated in the presence of Star NAT. Our data identify antisense transcription as an additional mechanism involved in the regulation of steroid biosynthesis

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.Published versio

An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics

Aimed at degradation prognostics of a rolling bearing, this paper proposed a novel cumulative transformation algorithm for data processing and a feature fusion technique for bearing degradation assessment. First, a cumulative transformation is presented to map the original features extracted from a vibration signal to their respective cumulative forms. The technique not only makes the extracted features show a monotonic trend but also reduces the fluctuation; such properties are more propitious to reflect the bearing degradation trend. Then, a new degradation index system is constructed, which fuses multidimensional cumulative features by kernel principal component analysis (KPCA). Finally, an extreme learning machine model based on phase space reconstruction is proposed to predict the degradation trend. The model performance is experimentally validated with a whole-life experiment of a rolling bearing. The results prove that the proposed method reflects the bearing degradation process clearly and achieves a good balance between model accuracy and complexity

Electron Transport and CO Sensing Characteristics of Fe(II) Porphyrin with Single-Walled Carbon Nanotube Electrodes

Electron transport and carbon monoxide (CO) sensitive characteristics of Fe­(II) porphyrin (PP-Fe) with two single-walled carbon nanotube electrodes (SWCNTs) were studied using nonequilibrium Green’s function (NEGF) formalism combined first-principles density functional theory (DFT). When PP-Fe is connected to SWCNT electrodes in diagonal configuration, owing to the fact that the electron passes through the Fe center, it shows higher CO sensitivity than that in para connection mode. Meanwhile, the PP-Fe sensor with armchair SWCNTs is more sensitive to CO than that in zigzag junction, since the coupling interaction between central molecule and armchair SWCNT electrodes is stronger. The PP-Fe sensor anchored on armchair SWCNTs in diagonal configuration can reach a current on–off ratio of 2.1 × 10⁴ in response to the chemisorption of CO, showing better CO sensitive characteristics than that with zigzag SWCNTs and metallic electrodes. With the development of experimental techniques, it is completely possible to fabricate this kind of CO sensor by experimental means

Image Retrieval based on the Multiwavelets Texture-Spatial Features

Abstract: A new retrieval algorithm based on the texture-spatial texture using the GHM multiwavelets is presented. In order to describe the important visual information of image, we design the improved multiwavelets quantization map that can depict the important visual information for the multiwavelets sub-bands. Furthermore, the visual spatial histogram of multiwavelets quantization map is used as the texture-spatial features that denote the global texture information of image. At the same time, the local binary pattern is used to describe the local texture-spatial feature for the low frequency multiwavelets sub-band. Finally, the similarity of visual spatial histogram is computed by the spatial-weighted distance. Experiments indicate that this method gives better performance than looseness texturespatial algorithm and MTH algorithm in the natural image retrieval