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Case Studies, Results and Validation
This paper proposes condition based monitoring of machines using fused features. There are six major phases of this framework. In the first phase, all kind of sensor data... View more
Abstract: Several data-driven methods such as signal processing and machine learning exist separately to analyze non-linear and non-stationary data but their performance degrades d... View more
Several data-driven methods such as signal processing and machine learning exist separately to analyze non-linear and non-stationary data but their performance degrades due to insufficient information in the real-time application. In order to improve the performance, this paper proposes a novel feature extraction method using fusion of hand-crafted (low-level) features and high-level features, followed by feature extraction/selection on fused features. Local energy-based hand-crafted features have been derived from empirical mode decomposition, and high-level features have been extracted from the deep neural network. A method is also proposed for reduction of massive data points in the samples. The proposed scheme has studied the effect of variation in the number of extracted/selected features. The effectiveness of the proposed scheme is validated through three case studies: a) on acoustic dataset collected from the reciprocating type air compressor, b) on vibration dataset collected from deep groove ball bearing, and c) on steel plate faults dataset. The classification accuracy on acoustic dataset are obtained as high as 100.0%, 99.78%, and 99.78% using the random forest, linear support vector machine, and radial basis function support vector machine, respectively, with 5-fold cross-validation. Similarly, on vibration dataset obtained accuracies are 100.0%. The proposed scheme has been compared with ten conventional methods on five-fold cross-validation. These experimental results show considerable improvement in the prediction performance of machine conditions using the proposed scheme.
Published in: IEEE Sensors Journal ( Volume: 20 , Issue: 15 , 01 August 2020 )
This paper proposes condition based monitoring of machines using fused features. There are six major phases of this framework. In the first phase, all kind of sensor data... View more
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Condition Based Monitoring (CBM) methods are widely practiced in various industries for rotating systems because they operate in stringent conditions and therefore subjected to unpredicted faults. The unpredicted faults may cause hazardous accidents and financial losses in the industries. The CBM prevents such unpredicted failures which results in the reduction of maintenance cost and increases machine life-time. CBM helps in improvement of reliability, security and total productivity. There are two types of maintenance techniques, corrective maintenance techniques and preventive maintenance technique [1]. CBM is a preventive maintenance technique which has proved to be more economical and production friendly [3], [4]. In [5], Martin has discussed some other traditional failure and preventive maintenance techniques. CBM methods can work on various types of signals i.e. vibration signal [6], acoustic signal [7], temperature signal [9] etc. The performance measure is a major aspect for real-time CBM methods. However, this aspect is difficult to obtain due to varying sensor data acquired from industrial machine(s) [2].
2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
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Several data-driven methods such as signal processing and machine learning exist separately to analyze non-linear and non-stationary data but their performance degrades due to insufficient information in the real-time application. In order to improve the performance, this paper proposes a novel feature extraction method using fusion of hand-crafted (low-level) features and high-level features ...
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23. Dez. 2022 Fellow superintendents from more than 30 nearby school districts came together Thursday to show support for the leader of schools in Wayland, Massachusetts, after racist graffiti targeted him. A message equating Superintendent Omar Easy, who is Black, with a racist slur was written at the Wayland Community Pool, located next to Wayland High School. Police and school officials are investigating….
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Several data-driven methods such as signal processing and machine learning exist separately to analyze non-linear and non-stationary data but their performance degrades due to insufficient information in the real-time application. In order to improve the performance, this paper proposes a novel feature extraction method using fusion of hand-crafted (low-level) features and high-level features ...
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