Fault diagnosis systems for rotating machines operating with fluid-film bearings

Author(s):  
Ivan Stebakov ◽  
Alexey Kornaev ◽  
Sergey Popov ◽  
Leonid Savin

The paper deals with the application of deep learning methods to rotating machines fault diagnosis. The main challenge is to design a fault diagnosis system connected with multisensory measurement system that will be sensitive and accurate enough in detecting weak changes in rotating machines. The experimental part of the research presents the test rig and results of high-speed multisensory measurements. Six states of a rotating machine, including a normal one and five states with loosened mounting bolts and small unbalancing of the shaft, are under study. The application of deep network architectures including multilayer perceptron, convolutional neural networks, residual networks, autoencoders and their combination was estimated. The deep learning methods allowed to identify the most informative sensors, then solve the anomaly detection and the multiclass classification problems. An autoencoder based on ResNet architecture demonstrated the best result in anomaly detection. The accuracy of the proposed network is up to 100% while the accuracy of an expert is up to 65%. A one-dimensional convolutional neural network combined with a multilayer perceptron that contains a pretrained encoder demonstrated the best result in multiclass classification. The detailed fault detection accuracy with the determination of the specific fault is 83.3%. The combinations of known deep network architectures and application of the proposed approach of pretraining of the encoders together with using a block of inputs for one prediction demonstrated high efficiency.

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.


2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.


2021 ◽  
Vol 14 (2) ◽  
pp. 93
Author(s):  
Kristina Gorshkova ◽  
Victoria Zueva ◽  
Maria Kuznetsova ◽  
Larisa Tugashova

Author(s):  
Hanna Pamula ◽  
Agnieszka Pocha ◽  
Maciej Klaczynski

Every year billions of birds migrate between their breeding and wintering areas. As birds are an important indicator in nature conservation, migratory bird studies have been conducted for many decades, mostly by bird-ringing programmes and direct observation. However, most birds migrate at night, and therefore much information about their migration is lost. Novel methods have been developed to overcome this difficulty; including thermal imaging, radar, geolocation techniques, and acoustic recognition of bird calls. Many bird species are detected by their characteristic sounds. This method of identification occurs more often than by direct observation, and therefore recordings are widely used in avian research. The commonly used approach is to record the birds automatically, and to manually study the bird sounds in the recordings afterwards (Furnas and Callas 2015, Frommolt 2017). However, the tagging of recordings is a tedious and time-consuming process that requires expert knowledge, and, as a result, automatic detection of flight calls is in high demand. The first experiments towards this used energy thresholds or template matching (Bardeli et al. 2010, Towsey et al. 2012), and later on the machine and deep learning methods were applied (Stowell et al. 2018). Nevertheless, not many studies have focused specifically on night flight calls (Salamon et al. 2016, Lostanlen et al. 2018). Such acoustic monitoring could complement daytime avian research, especially when the field recording station is close to the bird-ringing station, as it is in our project. In this study, we present the initial results of a long-term bird audio monitoring project using automatic methods for bird detection. Passive acoustic recorders were deployed at a narrow spit between a lake and the Baltic sea in Dąbkowice, West Pomeranian Voivodeship, Poland . We recorded bird calls nightly from sunset till sunrise during the passerine autumn migration for 3 seasons. As a result, we collected over 3000 hours of recordings each season. We annotated a subset of over 50 hours, from different nights with various weather conditions. As avian flight calls are sporadic and short, we created a balanced set for training - recordings were divided into partially overlapping 500-ms clips, and we retained all clips containing calls and created about the same number of clips without bird sounds. Different signal representations were then examined (e.g. mel-spectrograms and multitaper). Afterwards, various convolutional neural networks were checked and their performance was compared using the area under the receiver operating characteristic curve (AUC) measure. Moreover, an initial attempt was made to take advantage of the transfer learning from image classification models. The results obtained by the deep learning methods are promising (AUC exceeding 80%), but higher bird detection accuracy is still needed. For a chosen bird species – Song thrush (Turdus philomelos) – we observed a correlation between calls recorded at night and birds caught in the nets during the day. This fact, as well as the promising results from the detection of calls from long-term recordings, indicate that acoustic monitoring of nocturnal birds has great potential and could be used to supplement the research of the phenomenon of seasonal bird migration.


Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2336
Author(s):  
Asif Khan ◽  
Hyunho Hwang ◽  
Heung Soo Kim

As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.


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