Bearing fault detection and diagnosis by fusing vibration data

This article presents a simple method for the detection and diagnosis of bearing faults, by fusing the information coming from two accelerometers. The method relies on three simple and intuitive features, extracted from the data coming from accelerometers placed at two different sites of the system under investigation. Our preliminary results indicate that by using simple statistical measures, such as the elements of the covariance matrix of the two sensors, faults at an early stage can be detected. In our the proposed scheme, the extracted features are fed to a k-nearest neighbour classifier for diagnosis purposes or to an ensemble of one-class detectors, if only the information from normal situation is available. As it is proved, based on experimental results, in both scenarios a remarkably high detection/diagnostic performance is achieved.Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

Acoustic emission localization on ship hull structures using a deep learning approach

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

Acoustic emission localization on ship hull structures using a deep learning approach

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

Acoustic emission localization on ship hull structures using a deep learning approach

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

Fault Diagnosis, Failure Prognosis and Fault Tolerant Control of Aerospace/Unmanned Aerial Systems

Fault-tolerant control and operation of complex unmanned and aircraft systems is an emerging technology intended to provide the designer and operator with flexibility, interoperability, sustainment and reliability under changing operational requirements or mission profiles. Moreover, it is intended to reconfigure online hardware and software to maintain the operational integrity of the system in the event of contingencies (fault/failure modes). This paper presents an hierarchical architecture that uses available sensor information, fault isolation, failure prognosis, system restructuring and controller reconfiguration. The fault tolerant control framework relies on prognostic information to reconfigure system components and preserve the operational integrity of the aircraft. The hierarchical structure starts at the lowest component level and migrates to the middle system/subsystem level ending with the final mission level. We illustrate the methodology using an electro-mechanical actuator (EMA).Godkänd; 2016; 20160531 (geonik)Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

Multi-Class Classification Approach for the Diagnosis of Broken Rotor Bars based on Air-Gap Magnetic Flux Density

In this paper, condition monitoring of induction machines using air-gap magnetic flux density spectrum via artificial neural networks is presented. The proposed scheme is chosen due to its effectiveness, simplicity, and low cost that used for the detection of broken rotor bar faults. The spectrum of the air-gap magnetic flux density is estimated using the Fast Fourier Transform, which can capture the fault related to harmonic components. The extracted information is then utilized by a machine-learning paradigm in a Multi-class classification approach for the detection of broken rotor bars, for both, adjacent and non-adjacent using artificial neural networks as a classification method. The obtained simulation results of the healthy and faulty conditions using finite elements prove the applicability of the proposed method

Multi-Class Classification Approach for the Diagnosis of Broken Rotor Bars based on Air-Gap Magnetic Flux Density

In this paper, condition monitoring of induction machines using air-gap magnetic flux density spectrum via artificial neural networks is presented. The proposed scheme is chosen due to its effectiveness, simplicity, and low cost that used for the detection of broken rotor bar faults. The spectrum of the air-gap magnetic flux density is estimated using the Fast Fourier Transform, which can capture the fault related to harmonic components. The extracted information is then utilized by a machine-learning paradigm in a Multi-class classification approach for the detection of broken rotor bars, for both, adjacent and non-adjacent using artificial neural networks as a classification method. The obtained simulation results of the healthy and faulty conditions using finite elements prove the applicability of the proposed method