Automatic band selection algorithm for envelope analysis

Fault detection and diagnosis in low speed rolling element bearings using inductive inference classification

Vibration time signals emitted by a defective rolling element bearing are often used to determine its health condition. For real-world measurements these fault signals may be weak compared to the remaining content in the measured signal causing increased difficulties to fault detection. One way of enhancing the fault detection is by demodulation at a filtered frequency band in which the Signal-to-Noise-Ratio (SNR) of the fault signal is high. In the present paper, we propose a method that uses the resonances obtained from Operational Modal Analysis (OMA) as center frequencies for the filter bands as it is assumed that the characteristic bearing fault frequencies modulate resonances or carrier frequencies close to them. An indicator is used to determine which of the resonances from the OMA enhance the fault signal the most and this selection is dependent on the bandwidth as well. The presented method is applied on signals from two experimental cases, an experimental test rig of a planetary gearbox and a wind turbine gearbox, and is compared to the Fast Kurtogram and Log-Cycligram to assess its performance for band selection. The results show that the presented method determines a suited band for band pass filtering which enhances the fault detection at the two complex use cases.

Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and mapping. In this paper, a crop superpixel-based affinity propagation (CS-AP) band selection method is proposed for crop identification and mapping in agriculture using HSIs. The approach begins by gathering crop superpixels; then, a spectral band selection criterion is developed by analyzing the variations in the spectral and spatial characteristics of crop superpixels. Finally, crop identification bands are determined through an efficient clustering approach, AP. Two typical agricultural hyperspectral data sets, the Salinas Valley data set and the Indian Pines data set, are selected for validation, each containing 16 crop classes, respectively. The experimental results show that the proposed CS-AP method achieves a mapping accuracy of 92.4% for the Salinas Valley data set and 88.6% for the Indian Pines data set. When compared to using all bands, two unsupervised band selection techniques, and three semi-supervised band selection techniques, the proposed method outperforms others with an improvement of 3.1% and 4.3% for the Salinas Valley and Indian Pines data sets, respectively. Indicate that the proposed CS-AP method achieves superior mapping accuracy by selecting fewer bands with greater crop identification capability compared to the other band selection methods. This research’s significant results demonstrate the potential of this approach in precision agriculture, offering a more cost-effective and timely solution for large-scale crop mapping and monitoring in the future.

Band selection (BS) is a method for optimizing feature selection, which aims to of reduce the computational complexity of processing hyperspectral image (HSI). However, there are many BS methods applied to image classification, target detection, and anomaly detection. Furthermore, the existing BS methods ignore the spatial structure of HSI. To solve the above problems, we proposed a dynamic programming-based BS method for hyperspectral unmixing. In this paper, we use the convex geometric structure of HSI in band space to project it into the subspace to obtain depth spectral features, then construct a dynamic programming model to select representative bands. To verify the effectiveness of the proposed method, experiments are conducted on three widely used datasets, and compared with three popular BS methods. The experimental results show that the proposed method has satisfactory performance in different evaluation indexes (including signal to reconstruction error (SRE), root mean square error (RMSE)) and three quantitative evaluations (average information entropy (AIE), average correlation coefficient (ACC) and average relative entropy (ARE)).

Rolling element bearings are critical mechanical components in rotating machinery. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper presents an algorithm using feed forward neural network for automated diagnosis of localized faults in rolling element bearings. Normal negative log-likelihood value and kurtosis value extracted from time-domain vibration signals are used as input features for the neural network. Trained neural networks are able to classify different states of the bearing with 100% accuracy. The proposed procedure requires only a few input features, resulting in simple preprocessing and faster training. Effectiveness of the proposed method is illustrated using the bearing vibration data obtained experimentally.

The high dimensionality of hyperspectral imagery is a huge challenge for remote sensing data processing. Band selection utilizes the most distinctive and informative band subset to reduce data dimensions. Although band selection can significantly alleviate the computational burden, the process itself may be time consuming because it needs to take all pixels into consideration, especially when the image spatial size is larger. An improved band similarity-based band selection method is proposed for hyperspectral imagery target detection, which includes four steps: (1) bad bands are removed by data preprocessing; (2) several selected pixels are used for band selection instead of using all the pixels to reduce the computational complexity; (3) hyperspectral imagery is analyzed for target detection; and (4) the number of selected bands is determined by adjusting the threshold of similarity metric, to ensure target detection operators have the best performance with selected bands. In the example, the well-known adaptive coherence estimator detector was used to evaluate the effectiveness of the proposed band selection method. The receiver operating characteristics curves were plotted to prove the proposed algorithm quantitatively. The experimental results show that our method can yield a better result in target detection than other band selection methods.

Multiobjective band selection approach via an adaptive particle swarm optimizer for remote sensing hyperspectral images

Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band selection which has been developed to effectively group highly correlated bands of hyperspectral images into high corrected modules. It uses a particle swarm optimization scheme, which is a well-known method to solve the optimization problems, to develop an effective feature extraction algorithm for hyperspectral imagery. After PSO method is applied to the band reduction of hyperspectral images, the proposed IFBS is applied to enhance the efficiency of band selection. The propose method is evaluated by MODIS/ASTER airborne simulator (MASTER) for land cover classification during the Pacrim II campaign. The performance of IFBS is validated by the supervised k-nearest neighbor (KNN) classifier. Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction. Compared to other band selection methods, IFBS can effectively select the most significant bands for the image classification of hyperspectral images.

Detection of localised defects in rolling element bearings via composite hypothesis test

Parameter analysis is a traditional and widely used method for the analysis of acoustic emission (AE) signals due to its simplicity and easy realisation. However, this approach has limitations and is not suitable for detailed analysis of the signals, and waveform-based methods are becoming attractive for the analysis of AE signals. This paper uses waveform-based analysis methods to investigate the time-frequency characteristics of AE signals arising from defects in rolling element bearings. This includes the composition of the signals, the generation mechanism of the defects and the influence of the operational condition of the bearing on these characteristics. The wavelet scalogram of the continuous wavelet transform is used to perform the qualitative analysis and the discrete wavelet transform is used for the quantitative analysis. The results demonstrate that the operational condition of the bearing will influence the time domain, the frequency domain and the time-frequency domain characteristics of the AE signals in various ways. The results also prove that the wavelet transform, and especially the wavelet scalogram, has great potential for the waveform-based analysis of AE signals from defects.

Constrained energy minimization (CEM) is one of the most widely used target detection methods due to its simplicity and effectiveness. From a mathematical perspective, the detection result can be regarded as the summation of bands weighted by the corresponding components of the CEM operator. Based on this point of view, we propose a supervised band selection idea for CEM target detection. Three different band selection methods for CEM (namely, BSC1, BSC2 and BSC3) are presented and compared. Some comparative experiments based on real hyperspectral data are conducted to evaluate the performance of these methods (the presented and other two methods). The results show that BSC3 is the most effective band selection method. The CEM detection result is quite acceptable using only a few of the bands selected by BSC3. BSC1 and BSC2 are also more accurate than other compared band selection methods when the number of selected bands is relatively large (>18 in our experiments).

There are several methods of automotive diagnostics used in services to detect a large variety of faults and damages of various parts of engines of internal combustion. Undoubtedly, they are effective, but they are simply unable to find all types of mechanical faults occurring during the operation. This is the reason why authors of this paper tried to use a special tool, which has been proven for years for detecting faults of rolling element bearing in rotating machinery. During their research, the authors tried to find valuable results by measuring vibration of various parts of engines. Three items were tested, a Diesel engine and two Otto motors. A large number of measurements have been taken at various speed, at different points, in different directions, with different parameter setup, etc. However, there was one setup which has been applied to all three engines. It is the measurement setup of vibration velocity, in the frequency range of 2 Hz-300 Hz. Valuable consequences have been found regarding the clogging of the air filters and the exhaust systems. As a conclusion the authors expressed their opinion, that, apart from the traditional diagnostic methods used in services, vibration measurements can also be useful, especially for detecting faults of rolling element bearings.

Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. In this paper, a novel multiobjective model is first built for band selection. In this model, two objective functions with a conflicting relationship are designed. One objective function is set as information entropy to represent the information contained in the selected band subsets, and the other one is set as the number of selected bands. Then, based on this model, a new unsupervised band selection method called multiobjective optimization band selection (MOBS) is proposed. In the MOBS method, these two objective functions are optimized simultaneously by a multiobjective evolutionary algorithm to find the best tradeoff solutions. The proposed method shows two unique characters. It can obtain a series of band subsets with different numbers of bands in a single run to offer more options for decision makers. Moreover, these band subsets with different numbers of bands can communicate with each other and have a coevolutionary relationship, which means that they can be optimized in a cooperative way. Since it is unsupervised, the proposed algorithm is compared with some related and recent unsupervised methods for hyperspectral image band selection to evaluate the quality of the obtained band subsets. Experimental results show that the proposed method can generate a set of band subsets with different numbers of bands in a single run and that these band subsets have a stable good performance on classification for different data sets.

Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition

Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue. Density peak clustering (DPC) provides an effective means for this purpose, referred to as DPC-based BS (DPC-BS). It uses two indicators, cluster density and cluster distance, to rank all bands for BS. This paper reinterprets cluster density and cluster distance as band local density (BLD) and band distance (BD) and also introduces a new concept called band prominence value (BPV) as a third indicator. Combining BLD and BD with BPV derives new band prioritization criteria for BS, which can extend the currently used DPC-BS to a new DPC-BS method referred to as band density prominence clustering (BDPC). By taking advantage of the three key indicators of BDPC, i.e., cut-off band distance bc, k nearest neighboring-band local density, and BPV, two versions of BDPC can be derived called bc-BDPC and k-BDPC, both of which are quite different from existing DPC-based BS methods in three aspects. One is that the parameter bc of bc-BDPC and the parameter k of k-BDPC can be automatically determined by the number of clusters and virtual dimensionality (VD), respectively. Another is that instead of using Euclidean distance, a spectral discrimination measure is used to calculate BD as well as inter-band correlation. The most important and significant aspect is a novel idea that combines BPV with BLD and BD to derive new band prioritization criteria for BS. Extensive experiments demonstrate that BDPC generally performs better than DPC-BS as well as many current state-of-the art BS methods.