An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data


Intelligent fault diagnosis may be a promising tool to accommodate mechanical huge knowledge due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In ancient intelligent diagnosis methods, but, the options are manually extracted depending on prior knowledge and diagnostic experience. Such processes use human ingenuity however are time-consuming and labor-intensive. Galvanized by the thought of unsupervised feature learning that uses artificial intelligence techniques to learn options from raw information, a two-stage learning methodology is proposed for intelligent diagnosis of machines. In the first learning stage of the strategy, sparse filtering, an unsupervised 2-layer neural network, is used to directly learn features from mechanical vibration signals. Within the second stage, softmax regression is employed to classify the health conditions primarily based on the learned options. The proposed methodology is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed methodology obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning options adaptively, the proposed technique reduces the need of human labor and makes intelligent fault diagnosis handle massive information additional easily.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Intelligent Power Sharing of DC Isolated Microgrid Primarily based on Fuzzy Sliding Mode Droop ManagementABSTRACT:Linear droop control will realize power sharing among generators in DC microgrid while not relying
PROJECT TITLE :Smart Monitoring Cameras Driven Intelligent Processing to Big Surveillance Video Data - 2018ABSTRACT:Video surveillance system has become a critical half in the safety and protection system of modem cities, since
PROJECT TITLE :Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces - 2018ABSTRACT:In this Project, we contemplate the potential of knowledge transmission in a system with a large number of radiating
PROJECT TITLE :Intelligent Spectrum Management Based on Transfer Actor-Critic Learning for Rateless Transmissions in Cognitive Radio Networks - 2018ABSTRACT:This Project presents an intelligent spectrum mobility management scheme
PROJECT TITLE :Maximum Power Point Tracking in Grid Connected Wind Plant by using Intelligent Controller and Switched Reluctance Generator - 2017ABSTRACT:This paper presents intelligent controllers as a most power point tracking

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry