Search results for "Fault detection and isolation"

Weather sensor fault detection in meteorological masts

2020

Wind power has become the world’s fastest growing renewable technology. The world-wide wind power installed capacity has exceeded 597 GW, and the new installations during the last three years was an average of 50 GW per year. A major issue with wind power system and with meteorological masts is the relatively high cost of operation and maintenance (OM). Wind turbines and sensor towers are hard-to-access structures, and they are often located in remote areas. That’s why continuous monitoring of wind turbine health using automated failure detection algorithms can improve turbine reliability and reduce maintenance costs by detecting failures before they reach a catastrophic stage and by elimin…

Artificial Intelligence in Monitoring and Diagnostics of Electrical Energy Conversion Systems

2020

Diagnostics and prognostics of electrical energy conversion systems are moving forward with the rapid development of IT and artificial intelligence possibilities. This also broadens the horizons for classical and advanced condition and operation monitoring techniques, resulting in more accurate fault detection, degradation prognosis and calculation of remaining life of energy conversion systems, utilized in every aspect and field of industry today. This paper gives an overview of the necessity for condition monitoring and diagnostics of the mentioned systems, explaining the classical and advanced techniques for diagnostics. Methodology to diagnose and prognose the energy conversion units, w…

Robust fault detection for switched systems with time-varying delay using delta operator approach

2014

Quantitative Rotor Broken Bar Evaluation in Double Squirrel Cage Induction Machines under Dynamic Operating Conditions

2013

Advanced monitoring techniques leading to fault diagnosis and prediction of induction machine faults, operating under non-stationary conditions have gained strength because of its considerable influence on the operational continuation of many industrial processes. In case of rotor broken bars, fault detection based on sideband components issued from currents, flux, instantaneous control or power signals under different load conditions, may fail due to the presence of inter-bar currents that reduce the degree of rotor asymmetry, especially for double squirrel cage induction motors. But the produced core vibrations in the axial direction, can be investigated to overcome the limitation of the …

Bearing fault detection based on time-frequency representations of vibration signals

2015

To prevent failures of a rolling bearing in the gearbox drive system, acceleration sensors are used to detect fault-related signals of the bearing. It is a big challenge to observe and identify signals caused by bearing defects in the time domain or the frequency spectrum by a conventional Fourier analysis. The time-frequency representation of the fault-related signals implemented by the windowed Fourier transform is studied in this work. It is shown that the fault characteristic frequencies can be clearly identified in the time-frequency spectrum if a fault occurs in the bearing of the gearbox at different speeds. Otherwise, the shaft frequency and its multiples are the main harmonics in t…

Gear classification and fault detection using a diffusion map framework

2015

This article proposes a system health monitoring approach that detects abnormal behavior of machines. Diffusion map is used to reduce the dimensionality of training data, which facilitates the classification of newly arriving measurements. The new measurements are handled with Nyström extension. The method is trained and tested with real gear monitoring data from several windmill parks. A machine health index is proposed, showing that data recordings can be classified as working or failing using dimensionality reduction and warning levels in the low dimensional space. The proposed approach can be used with any system that produces high-dimensional measurement data. peerReviewed

Cognitive self-healing system for future mobile networks

2015

This paper introduces a framework and implementation of a cognitive self-healing system for fault detection and compensation in future mobile networks. Performance monitoring for failure identification is based on anomaly analysis, which is a combination of the nearest neighbor anomaly scoring and statistical profiling. Case-based reasoning algorithm is used for cognitive self-healing of the detected faulty cells. Validation environment is Long Term Evolution (LTE) mobile system simulated with Network Simulator 3 (ns-3) [1, 2]. Results demonstrate that cognitive approach is efficient for compensation of cell outages and is capable to improve network coverage. Anomaly analysis can be used fo…