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Machinery fault diagnosis is an attractive but challenging task, especially for low-speed conditions. Therefore, a new discriminative approach that introduces robust principal component analysis (RPCA) and multikernel to deep neural networks is proposed to perform intelligent fault diagnosis. First, RPCA is applied to extract fault signals from extreme background noise based on its sensitivity to grossly corrupted data. Second, two cascaded multikernel principal component analysis stages with additional robustness to distortions in feature extraction are used to enhance the energy of spectrum symptom and overcome the tricky issues of low-speed machinery. Especially, the multikernel is introduced into the basic PCA filters to learn the data-adapting convolution filter and gain additional robustness to nonlinearity in the signal. Finally, the proposed method is demonstrated on signals from laboratory tests (with a slightly damaged defect in a bearing) and structural fault data, outperforming those of traditional machine learning and classical deep learning methods. Moreover, hidden information of the network is visualized to analyze the reasons for its high performance. [ABSTRACT FROM AUTHOR]

Raw vibration signals poorly perform in industrial bearing fault diagnosis because impulse features are damped and masked by disturbances and noises. Fault diagnosis is more challenging due to weak features. This work presents a signal filtering and fault characteristic enhancement method based on reconstruction adaptive determinate stationary subspace filtering (Rad-SSF) and enhanced third-order spectrum to address the above problems. In particular, Rad-SSF reconstructs an adaptive self-determined, decomposed vibration signal trajectory matrix to obtain non-stationary signals. Then, the filtered signal with the best fault characteristics is extracted according to kurtosis. A 1.5-dimensional third-order energy spectrum is performed to enhance the fault characteristics by strengthening the fundamental frequency and eliminating non-coupling harmonics. Finally, the dominant frequency in the spectrum is contrasted to recognize fault diagnosis, referring to theoretical fault characteristic frequency. The feasibility and effectiveness of the proposed method are demonstrated by simulation and engineering signals under different conditions. [ABSTRACT FROM AUTHOR]

Bearing fault impulses are easily submerged by background noise, resulting in the inconspicuous fault feature and affecting the accuracy of the fault diagnosis. This paper presents a novel method for bearing the fault feature enhancement and diagnosis based on the statistical filtering and the 1.5-dimensional symmetric difference analytic energy operator (1.5D-SDAEO) to solve the problem. 1). The statistical filtering is used to filter the background noise under the standard distinction index. 2). The 1.5D-SDAEO is used to enhance the signal impulse, suppress the residual noise, and improve the SNR. 3). The dominant frequency in the energy spectrum is compared with the rolling bearing fault characteristic frequency to the fault diagnosis. The feasibility and the superiority of the presented method are verified by the simulation, engineering, and comparison experiments. All results show that the presented method can effectively enhance the fault feature and accurately diagnose the rolling bearing fault. [ABSTRACT FROM AUTHOR]

A novel method of intelligent diagnosis is proposed for structural faults in low-speed rotating machinery. It uses a hybrid scheme to automatically identify health states in a complex mechanical system by combining an improved mode decomposition approach, a Gramian angular summation field, and a convolutional neural network. The proposed method is tested with a data set affected by noise to evaluate its performance and generalizability that are both essential in fault diagnosis. Experimental results show that the proposed scheme is superior to traditional machine learning methods. It not only provides a more efficient and widely applicable way to learn intrinsic fault signatures, but it also has enhanced feature learning capability to improve precision and generalizability. Finally, the reasons for the method’s high performance are analyzed to determine the best general features for an adaptive classifier that is generalizable to diverse operating conditions. [ABSTRACT FROM AUTHOR]

Amorphous indium–gallium–zinc oxide (a-IGZO) thin-film transistors (TFTs) with high mobility are highly desirable to achieve high-speed operation in active-matrix displays and large-area sensors. Only a few works provide potential solutions. In this letter, we have embedded carbon nanotubes (CNTs) into sputtered a-IGZO film to form a-IGZO/CNT/a-IGZO (ACA) sandwiched channel. In the ACA channel, the CNT percolation networks connected by the a-IGZO film work as high-speed carrier paths to enable faster transport of carriers during the ON state, while it does not degrade the leakage performance during the OFF state. The type and the embedding location of the CNT percolation network are critical to determine the ACA device performance, which is analyzed and verified by experiment and simulation. The optimum ACA design has approximately doubled the mobility and the ON-current density of the TFT. The design owns relatively better uniformity and provides a high-speed TFT solution for the advanced electronics. [ABSTRACT FROM AUTHOR]

Fault feature extracted from fault signals plays a vital role in fault detection and diagnosis. However, in the early stage of defect, the fault signals (amplitudes and energy) are very weak and overwhelmed by large noise, and it is very difficult to extract fault feature effectively. To handle this problem, in this paper, an automatic filtering method based on an improved genetic algorithm (IGA) for extracting fault signals is presented. This method improves the binary coding generation of GA, so that each binary value matches a frequency of a fault signal frequency spectrum. Based on designed “statistical information evaluation function” as IGA fitness function, the similarity between normal state signal (noise signal) and IGA extracted noise signal is evaluated, and IGA employs genetic operations to search the optimal noise binary string. Negating noise binary string can obtain optimal fault signal binary string. It will then be multiplied by abnormal state vibration signal frequency spectrum, to yield a new frequency spectrum that only has fault components. This proposed method does not need to seek center frequency and bandwidth, and can automatically remove noise. The effective and advanced performance of the proposed method is evaluated by three different rolling bearing defects (outer race defect, inner race defect, and roller defect) in rolling bearing on rotating machine experimental bench and gearbox. Compared with rolling bearing fault characteristic frequency and results of denoising methods of bandpass filtering and wavelet decomposition, the proposed method can automatically filter noise and effectively extract fault signal. It proves that this method is feasible, effective, and advanced. [ABSTRACT FROM PUBLISHER]