[教程] MATLAB技术论坛数据挖掘公开课数据挖掘中的数据预处理

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several faults in different scenarios is a desirable tool. A promising classification technique is the 𝐾-means algorithm. It is a well-known signal classification technique that has been successfully utilized in many applications such as neuro- science [24], structural engineering [25], and mechanics [26]. This approach provides a high accuracy and good generaliza- tion for a small number of features; besides its computational cost during and after its design is relatively low.
In this work, a methodology to detect automatically the BRB fault in induction motors using vibration signals is presented. The proposal considers the analysis of both the startup transient and the steady state of operation, which is very important since the induction motor may be subject to both scenarios in real applications; besides, an imple- mentation into a field programmable gate array (FPGA) is also presented as system-on-chip (SoC) solution. This allows offering a system for online and continuous monitoring. Regarding the BRB condition, half, one, and two bars are considered. For the analysis, the Shannon entropy is used as a measure of the information contained in the vibration signals. This information presents changes associated with the fault. Then, the obtained entropy values are classified for automatic diagnosis using the 𝐾-means algorithm. The results show that the proposal can be a low-complex and suitable tool for BRB detection in both the startup transient and the steady state of operation.
but it can cause an excessive vibration, a change in current consumption, and higher thermal stress with catastrophic consequences if the situation is not solved at early stages [4, 5]. In this regard, condition monitoring equipment has become an essential tool in many industrial areas. Yet, this task is very challenging because depending on the application the motor may be subject to transient and/or steady (nominal) regimes of operation, which changes its mechanical and electrical conditions by affecting and limiting the performance of equipment that only operates in a specific regime. From this point of view, an online and real-time monitoring system for an early detection of BRB in transient and steady regimes is a needed equipment in many industrial areas, since it will allow

scheduling maintenance operations in order to minimize its negative impact as well as saving time and money.
During the last decade, several vibration and current analysis-based processing techniques for BRB detection have been proposed. The conventional signal processing technique used to perform this task is the fast Fourier transform (FFT) [6–8]. However, it is limited in its capability for extracting features from signals that exhibit nonlinear and nonstation- ary characteristics, besides being susceptible to noise, making a correct identification of features related to the BRB fault difficult [9]. More recently, other powerful signal processing techniques, such as multiple signal classification (MUSIC) algorithm [10, 11], wavelet transform (WT) [9, 12–16], Empir- ical mode decomposition combined with Hilbert transform known as Hilbert-Huang transform (HHT) [16], and Wigner- Ville distribution (WVD) [17], have been used for BRB detec- tion. Nevertheless, although prominent results have been obtained, the aforementioned signal processing techniques present some unresolved difficulties. For instance, MUSIC requires a priori knowledge of the interest frequencies and consumes significant computational resources [11]. The WT capabilities are significantly degraded in noisy signals, and the mother wavelet has to be appropriately chosen to obtain reliable results [4]. On the other hand, the WVD introduces cross-term interference in the estimated signal components, which inhibits the efficient estimation of the instantaneous frequencies, besides suffering aliasing problem [18]. The HHT suffers from the mode mixing effect, which means that waves with the same frequency are assigned to different intrinsic mode functions, affecting the accurate estimation of the instantaneous frequencies. In general, many advantages and disadvantages of the aforementioned techniques may be further discussed; yet, from a monitoring equipment viewpoint, two aspects become important. The first one is the performance capabilities; it means that the equipment does not degrade its performance when it analyzes transient or stationary signals. This desirable feature may be achieved either using a nonsusceptible signal processing technique or using different techniques for each scenario. The second one is the complexity since it may compromise the online analysis if low-end digital signal processors are used. In this regard, it


Inductions motors are widely used in many applications because of their easy maintenance, ruggedness, low cost, versatility, and ease control [1]. During their service life, they are subject to unavoidable failures as a result of mechanical, environmental, thermal, and electrical stresses [2]. These faults such as bearing faults, air gap eccentricity, and broken rotor bars (BRBs) can yield a reduction on production, prod- uct quality, and an increase on costs, besides being a hazard for people and machinery [3]. Among the different faults that can occur in induction machines, BRB is a silent failure that allows operating the motor with apparent normality,

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