A sensitive spectrum entropy-assisted Bayesian online anomaly inference method for bearing incipient degradation dynamic detection.

Incipient degradation dynamic detection is crucial for preventing serious accidents in the context of rolling bearing online automatic condition monitoring and preventive maintenance. This article presents a novel framework, cyclostationarity-sensitive spectrum fuzzy entropy-assisted Bayesian online anomaly inference (CSFE-BOAI), to address this challenge. A new health index, CSFE, is first defined by performing the fuzzy entropy measure on the extracted cyclostationarity-sensitive spectra to promote incipient-degradation sensitivity and robustness to interferences.

Knowledge correlation graph-guided multi-source interaction domain adaptation network for rotating machinery fault diagnosis.

Leveraging generalized knowledge from multiple source domains with rich labels to the target domain without labeled data is a more realistic and challenging issue compared with single-source domain adaptation. Furthermore, the distribution discrepancies between each source domain and the expansion of data categories increase the difficulty of aligning each source domain with the target domain. To alleviate these issues, a knowledge correlation graph-guided multi-source interaction domain adaptation network (KCGMIDAN) is developed for rotating machinery fault diagnosis.

Multiband weights-induced periodic sparse representation for bearing incipient fault diagnosis.

Faulty impulses from incipient damaged bearings are typically submerged in harmonics, random shocks, and noise, making incipient fault diagnosis challenging. The prerequisite to this problem is the robust estimation of faulty impulses; thus, this paper proposes a multiband weights-induced periodic sparse representation (MwPSR) method. Firstly, a multiband weighted generalized minimax-concave induced sparse representation (MwGSR) approach is presented to accelerate the sparse approximation process and eliminate the interference components.

Substrate adhesion determines migration during mesenchymal cell condensation in chondrogenesis.

Mesenchymal condensation is a prevalent morphogenetic transition that is essential in chondrogenesis. However, the current understanding of condensation mechanisms is limited. In vivo, progenitor cells directionally migrate from the surrounding loose mesenchyme towards regions of increasing matrix adherence (the condensation centers), which is accompanied by the upregulation of fibronectin.

A deep reinforcement transfer convolutional neural network for rolling bearing fault diagnosis.

Deep neural networks highly depend on substantial labeled samples when identifying bearing fault. However, in some practical situations, it is very difficult to collect sufficient labeled samples, which limits the application of deep neural networks in practical engineering. Therefore, how to use limited labeled samples to complete fault diagnosis tasks is an urgent problem.

Periodicity-enhanced sparse representation for rolling bearing incipient fault detection.

Incipient fault detection of rolling bearings is a challenging task since the weak fault features are disturbed by heavy background noise. This paper develops a periodicity-enhanced sparse representation method to address this issue. Firstly, periodicity-enhanced basis pursuit denoising (PBPD) is proposed by the theoretical derivation.

Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO.

Intelligent fault diagnosis techniques cross rotating machines have great significances in theory and engineering For this purpose, this paper presents a novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO). First, novel stacked auto-encoder (NSAE) model is designed with scaled exponential linear unit (SELU), correntropy and nonnegative constraint. Then, NSTAE is constructed using NSAE and parameter transfer strategy to enable the pre-trained source-domain NSAE to adapt to the target-domain samples.

Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet.

Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT.