An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions.

Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions.

Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG.

Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications.

Adaptive Sparse Detector for Suppressing Powerline Component in EEG Measurements.

Powerline interference (PLI) is a major source of interference in the acquisition of electroencephalogram (EEG) signal. Digital notch filters (DNFs) have been widely used to remove the PLI such that actual features, which are weak in energy and strongly connected to brain states, can be extracted explicitly. However, DNFs are mathematically implemented via discrete Fourier analysis, the problem of overlapping between spectral counterparts of PLI and those of EEG features is inevitable.

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks.

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs) is proposed. In the proposed methodology, EEG waveforms of classes 1 and 2 are segmented into subsignals, and 140 experimental samples were achieved for each type of EEG signal.

Adaptive filtering enhanced windowed correlated kurtosis for multiple faults diagnosis of locomotive bearings.

Compound faults diagnosis of locomotive bearings are still a challenge especially when the multi-fault impulses share the common resonant frequency. In this paper, an adaptive filtering enhanced windowed correlated kurtosis (WCK) method is proposed to isolate and identify each fault mode. A concept termed flexible analytical wavelet transform (FAWT) spectrum is defined to construct proper FAWT basis such that high signal-to-noise (SNR) is obtained in the filtered signal.

Sparsity-based signal extraction using dual Q-factors for gearbox fault detection.

Early detection of faults developed in gearboxes is of great importance to prevent catastrophic accidents. In this paper, a sparsity-based feature extraction method using the tunable Q-factor wavelet transform with dual Q-factors is proposed for gearbox fault detection. Specifically, the proposed method addresses the problem of simultaneously extracting periodic transients and high-resonance component from noisy data for the gearboxes fault detection purpose.

Health State Monitoring of Bladed Machinery with Crack Growth Detection in BFG Power Plant Using an Active Frequency Shift Spectral Correction Method.

Power generation using waste-gas is an effective and green way to reduce the emission of the harmful blast furnace gas (BFG) in pig-iron producing industry. Condition monitoring of mechanical structures in the BFG power plant is of vital importance to guarantee their safety and efficient operations. In this paper, we describe the detection of crack growth of bladed machinery in the BFG power plant via vibration measurement combined with an enhanced spectral correction technique.

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents.