Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning.

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets

Gearbox fault diagnosis using improved feature representation and multitask learning

A gearbox is a critical rotating component that is used to transmit torque from one shaft to another. This paper presents a data-driven gearbox fault diagnosis system in which the issue of variable working conditions namely uneven speed and the load of the machinery is addressed. Moreover, a mechanism is suggested that how an improved feature extraction process and data from multiple tasks can contribute to the overall performance of a fault diagnosis model. The variable working conditions make a gearbox fault diagnosis a challenging task. The performance of the existing algorithms in the literature deteriorates under variable working conditions. In this paper, a refined feature extraction technique and multitask learning are adopted to address this variability issue. The feature extraction step helps to explore unique fault signatures which are helpful to perform gearbox fault diagnosis under uneven speed and load conditions. Later, these extracted features are provided to a convolutional neural network (CNN) based multitask learning (MTL) network to identify the faults in the provided gearbox dataset. A comparison of the experimental results of the proposed model with that of several already published state-of-the-art diagnostic techniques suggests the superiority of the proposed model under uneven speed and load conditions. Therefore, based on the results the proposed approach can be used for gearbox fault diagnosis under uneven speed and load conditions

Clustering Techniques for Software Engineering

Software industries face a common problem which is the maintenance cost of industrial software systems. There are lots of reasons behind this problem. One of the possible reasons is the high maintenance cost due to lack of knowledge about understanding the software systems that are too large, and complex. Software clustering is an efficient technique to deal with such kind of problems that arise from the sheer size and complexity of large software systems. Day by day the size and complexity of industrial software systems are rapidly increasing. So, it will be a challenging task for managing software systems. Software clustering can be very helpful to understand the larger software system, decompose them into smaller and easy to maintenance. In this paper, we want to give research direction in the area of software clustering in order to develop efficient clustering techniques for software engineering. Besides, we want to describe the most recent clustering techniques and their strength as well as weakness. In addition, we propose genetic algorithm based software modularization clustering method. The result section demonstrated that proposed method can effectively produce good module structure and it outperforms the state of the art methods.

Assessment of Flood Hazard in Climatic Extreme Considering Fluvio-Morphic Responses of the Contributing River: Indications from the Brahmaputra-Jamuna’s Braided-Plain

Climate change is expected to raise river discharge and sea level in the future, and these near-term changes could alter the river flow regime and sedimentation pattern of future floods. Present hazard assessment studies have limitations in considering such morpho-dynamic responses in evaluating flood hazards or risks. Here, we present a multi-model-based approach to quantify such potential hazard parameters influenced by climate change for the most vulnerable communities living on river bars and islands of the Brahmaputra–Jamuna River. River flood-flow and flood wave propagation characteristics are predicted to be affected by changing temporal distribution patterns of precipitation as a result of enhanced global warming. Increased incidences of large multi-peak floods or uncommon floods resulting in long-duration floods driven by sea-level rise may happen as a result of this. To assess it, we have set up a hydromorphic model, Delft3D, for the Brahmaputra–Jamuna River forced by upstream flow, generated from a hydrological model SWAT, over the Brahmaputra basin. The simulations cover moderate, wettest, and driest conditions of the RCP8.5 scenario, and the results reflect the flooding consequences of the near-future, mid-century, and end-century. Floods in the Brahmaputra–Jamuna River are becoming more severe, frequent, and long-lasting, as a result of climate change, and are expected to last until the end of November rather than the current September timeline. While assessing the hazard, we found that the pattern and timing of the flood are as equally important as the peak of the flood, as the river continuously adjusts its cross-sectional area with the flow. The study also demonstrates that, depending on their location/position, climate-induced hazards can affect sand bars/islands disproportionally. The high flood depth, duration, and sedimentation have a significant impact on the sand bars downstream of the river, making them more vulnerable

Assessment of Flood Hazard in Climatic Extreme Considering Fluvio-Morphic Responses of the Contributing River: Indications from the Brahmaputra-Jamuna’s Braided-Plain

Climate change is expected to raise river discharge and sea level in the future, and these near-term changes could alter the river flow regime and sedimentation pattern of future floods. Present hazard assessment studies have limitations in considering such morpho-dynamic responses in evaluating flood hazards or risks. Here, we present a multi-model-based approach to quantify such potential hazard parameters influenced by climate change for the most vulnerable communities living on river bars and islands of the Brahmaputra–Jamuna River. River flood-flow and flood wave propagation characteristics are predicted to be affected by changing temporal distribution patterns of precipitation as a result of enhanced global warming. Increased incidences of large multi-peak floods or uncommon floods resulting in long-duration floods driven by sea-level rise may happen as a result of this. To assess it, we have set up a hydromorphic model, Delft3D, for the Brahmaputra–Jamuna River forced by upstream flow, generated from a hydrological model SWAT, over the Brahmaputra basin. The simulations cover moderate, wettest, and driest conditions of the RCP8.5 scenario, and the results reflect the flooding consequences of the near-future, mid-century, and end-century. Floods in the Brahmaputra–Jamuna River are becoming more severe, frequent, and long-lasting, as a result of climate change, and are expected to last until the end of November rather than the current September timeline. While assessing the hazard, we found that the pattern and timing of the flood are as equally important as the peak of the flood, as the river continuously adjusts its cross-sectional area with the flow. The study also demonstrates that, depending on their location/position, climate-induced hazards can affect sand bars/islands disproportionally. The high flood depth, duration, and sedimentation have a significant impact on the sand bars downstream of the river, making them more vulnerable

Inspection of Phytochemical Content and In Vitro Antioxidant Profile of Gnaphalium luteoalbum L.: An Unexplored Phytomedicine

Background:Oxidative stress is intensely linked with several pathological manifestations. Searching for medicinal plant with the superior safety profile for the treatment of oxidative stress related disorders are ongoing due to multiple unwanted effects associated with synthetic antioxidants. Therefore the purpose of this study was to examine the phytochemical content, in vitro antioxidant potentiality of crude methanol extract (CME), carbon tetrachloride fraction (CTF), petroleum ether fraction (PEF), chloroform fraction (CLF) and ethyl acetate fraction (EAF) of aerial parts of Gnaphalium luteoalbum (GL) L. Methods:The aerial parts of the GL were extracted with methanol followed by fractionation using carbon tetrachloride, petroleum ether, chloroform and ethyl acetate.The phytochemical screening of this plant was performed by using standard methods to evaluate the existence of alkaloids, carbohydrates, phenols, flavonoids, saponins, tannins, terpenoids and fixed oils.Antioxidant potentiality was estimated by, 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl (OH) and nitric oxide (NO) radical scavenging tests. Total antioxidant capacity (TAC), total phenolic content (TPC) and total flavonoid content (TFC) were also measured. Results: Phytochemical analysis of the aerial parts of GL confirmed the presence of carbohydrates, phenols, flavonoids and saponins in crude extract and its all fractions. The CME showed the highest scavenging activity (43.28%) with IC50 of 398.49 μg/mL in the DPPH radical scavenging test. The IC50 values of EAF, CME were statistically significant (P < 0.05, P < 0.01) with respect to ascorbic acid (ACA). For OH and NO radical scavenging tests maximum scavenging (48.39%, 69.64%) was also reported for CME compared to CTF, PEF, CLF and EAF. Compared to ACA, in case of OH and NO radical scavenging activities the IC50 values of CME were markedly significant (P < 0.01, P < 0.05). In the TAC test, CME showed the highest antioxidant activity (absorbance, 2.6 nm) related to other fractions. TPC was found to be the highest in the CME (115.96 mg of gallic acid equivalent/g of dried extract) rather than other fractions. The ranking order of CTF, PEF, CLF, EAF and CME for TFC was 48.67 < 55.75 < 65.29 < 71.35 < 82.29 mg quercetin equivalent/g of dried extract. Conclusion: The existing study suggested that CME of the aerial parts of GL can be used as a natural source of antioxidant which might be effective towards preventing or slowing oxidative stress related disorders