Intelligent Bearing Fault Diagnosis Based on Open Set Convolutional Neural Network
نویسندگان
چکیده
Traditional data-driven intelligent fault diagnosis methods have been successfully developed under the closed set assumption (CSA). CSA-based assumes that types in test are consistent with training set, which can achieve high accuracy, but this is generally not valid real-world industrial applications where collection of data often limited. As it unrealistic to assume will cover all types, application classifier may fail when contains unknown because probability input samples belonging cannot be obtained. To solve problem how accurately identified, paper further studies open (OSA) diagnosis. We propose an convolutional neural network (OS-CNN) method and apply our OS-CNN model improved OpenMax as a deep detect types. The overall performance was significantly able effectively tighten boundary known classes limit open-space risk for based on distance modeling. effectiveness proposed verified by experimental four different bearing datasets. Compared state-of-the-art OSA method, only realize correct classification classes, also classes.
منابع مشابه
Intelligent Pearl Disease Diagnosis Based on Rough Set - Neural Network
In view of large amount of monitoring data for Pearl disease, complexity of network structure of the traditional diagnostic neural network method, validity of disease data issues and slow training, this paper introduces the rough set theory to intelligent Pearl disease diagnosis. A method for disease diagnostics is proposed based on rough set neural network. The rough set is used to remove the ...
متن کاملNeural-network-based motor rolling bearing fault diagnosis
Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the U.S. into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to moni...
متن کاملConvolutional Neural Net and Bearing Fault Analysis
There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis. In this paper, rather than preprocessing vibration signals to denoise or extract features, we investigate the usage of CNNs on raw signals; in particular, we test the accuracy of CNNs as classifiers on bearing fault data, by varying the configurations of the CNN from one-lay...
متن کاملAn Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network
As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying th...
متن کاملNon-melanoma skin cancer diagnosis with a convolutional neural network
Background: The most common types of non-melanoma skin cancer are basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). AKIEC -Actinic keratoses (Solar keratoses) and intraepithelial carcinoma (Bowen’s disease)- are common non-invasive precursors of SCC, which may progress to invasive SCC, if left untreated. Due to the importance of early detection in cancer treatment, this study aimed...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematics
سال: 2022
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math10213953