-This paper develops a novel spectrum enhancement method to simplify the spectrum representation. It uses a forward synchronous transform to convert three phase current signals into the two current signals in the synchronous reference frame. The spectrum obtained by applying Fast Fourier Transform (FFT) to one of the transformed current signals is then maximized by varying the initial phase in ...

The fault diagnosis of the dissolved gas analysis (DGA) of the power transformer is to be enhanced than previous adopted techniques; this paper proposes a novel adaptive neuro Fuzzy inference system for the incipient fault recognition through enhanced approach. Complying with the practical DGA records and associated fault causes as much as possible, an ANFIS algorithm is presented to establish ...

Deregulation of power industry in the US and energy crisis in some parts of the world put forward formidable challenges that need to be overcome for successful operation of power networks in the new environment. Monitoring of electric power systems in real-time for reliability, aging status, and presence of incipient faults requires distributed processing of vast amounts of data from distribute...

In this paper, a robust algorithm for fault diagnosis of power system equipment based on a failure-sensitive matrix (FSM) is presented. The FSM is a dynamic matrix structure updated by multiple measurements (online) and test results (offline) on the systems. The algorithm uses many different artificial intelligence and expert system methods for adaptively detecting the location of faults, emerg...

Abstract The Dissolved Gas Analysis (DGA) is an important way and now extensively used to detect incipient faults in oil-immersed transformers. Some conventional methods for DGA, such as the Three-Ratio Method, usually misunderstood gas data failed accurately multiple fault combinations. Recently, most researchers investigating DGA have utilized machine learning algorithms improve accuracy of d...

Abstract Early Late Cretaceous uplift of Provence gave rise to the Durance Isthmus. In present study, U–Pb dating on calcite compressional structures related Pyrenean foreland deformation in shows that N‐S shortening occurred coeval with emersion Isthmus, through development combined top‐to‐the‐North NW thrusts between 97 and 90 Ma. This large‐scale event, recorded from Pyrenees Middle‐East reg...

Gear and bearing faults are the main causes of gearbox failure. Till now, incipient fault diagnosis of these two components has been a problem and needs further research. In this context, it is found that Lucy–Richardson deconvolution (LRD) proved to be an excellent tool to enhance fault diagnosis in rolling element bearings and gears. LRD’s good identification capabilities of fault frequencies...

Lithium-ion batteries are shipped worldwide with many limitations implemented to ensure safety and to prevent loss of cargo. Many of the transportation guidelines focus on new batteries; however, the shipment requirements for used or degraded batteries are less clear. Current international regulations regarding the air transport of lithium-ion batteries are critically reviewed. The pre-shipping...

Industrial motors are subject to various faults which, if unnoticed, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based fault detection scheme ...

In condition monitoring of low speed rolling element bearings (REBs), traditional techniques involving vibration acceleration may not be able to detect a growing fault due to the low impact energy generated by the relative motion of the components. This study presents an experimental evaluation for incipient fault detection of low speed REBs by using an acoustic emission (AE) sensor and an acce...