A More Reliable Method for Monitoring the Condition of Three-Phase Induction Motors Based on Their Vibrations

Abstract

This paper is concerned with accurate, early, and reliable fault diagnosis using an enhanced vibration measurement technique based on short-time Fourier transform. The novelty of this work lies in detecting very low-phase imbalance-related faults. The energy contained within specified frequency bands centred on the rotor frequency and power supply frequency, and their sideband zones were calculated. The technique was firstly demonstrated by simulated signals and then verified by experimental measurements taken from two different-sized test rigs. The first one comprised a 1.1 kW variable speed three-phase induction motor with varying output load (no load, 25%, 50%, 75%, and 100% load). Two types of common faults were introduced: imbalance in one phase as the electrical fault and misalignment of load as the mechanical fault. The second test rig had a 3 kW three-phase induction motor again with varying load, and here the two seeded faults were: phase imbalance and one broken rotor bar. The measured energy levels in the test conditions were found to be affected by type of fault and fault severity. It is concluded that the proposed method offers a potentially reliable and computationally inexpensive condition monitoring tool which can be implemented with real-time monitoring systems.

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

“…One-versus-one RVM (2, 3, 8, 12) 97.5 Binary RVM (1, 9, 10, 11 vs. 4, 5, 6, 7) 95 One-versus-one RVM (1,9,10,11) 87.5 One-versus-one RVM (4,5,6,7) 87.5 Multistage Adaboost RVM 85 also cannot provide a satisfactory diagnosis results. The main reason is that there are too many fault types need to be classified by the second NN, and the interferences among different fault types have serious influence on the final decision.…”

“…Then, a binary RVM (1, 4, 5, 6, 7, 9, 10, 11 vs. 2, 3, 8, 12) model is established on G 1 and G 2 . In the second stage, the first cluster G 1 is divided into two clusters, G 11 (1,9,10,11) and G 12 (4,5,6,7), by the KFCM algorithm. Similarly, a binary RVM (1, 9, 10, 11 vs. 4, 5, 6, 7) model is established on G 11 and G 12 .…”

“…2 Signal processing plays an important role in fault diagnosis. Commonly used signal processing methods include short-time Fourier transform (STFT), wavelet transform (WT), Wigner-Ville distribution (WVD), and empirical mode decomposition (EMD), [2][3][4][5][6][7][8] among which EMD has attracted considerable attention recent years. The reason is that EMD is a data-driven technique, and it can decompose a multicomponent signal into a series of single components and a residual signal.…”

Bir Asenkron Motorun Mekanik Titreşim Sinyallerinin Ölçülerek Arıza Analizinin Yapılması

“…Burada, ham zaman sinyalini, örnek sayısını, ise örnekleme indeksini temsil etmektedir. CF yöntemi ile asenkron motorlarda arızanın erken teşhis edilmesinde yaygın olarak kullanılan bir metoddur (Shnibha et al, 2012). CF hesaplanırken sinyalin RMS değeri kullanılmaktadır.…”

Stator Fault Detection in Induction Motors by Autoregressive Modeling

“…Therefore early detection is critical. Unfortunately the induction motor may fail due to other mechanisms, such as bearings [7], the rotor due cracks in short-circuiting rings or broken bars [8], and eccentricity in the air gap [9]. In [10,11], some of the techniques commonly used for this purpose are shown.…”