Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine

Deep learning has attained remarkable achievements in diagnosing faults for rotary machineries. Capitalizing on the formidable learning capacity of deep learning, it has the potential to automate human labor and augment the efficiency of fault diagnosis in rotary machinery. These advantages have engendered escalating interest over the past decade. Although recent reviews of the literature have encapsulated the utilization of deep learning in diagnosing faults in rotating machinery, they no longer encompass the introduction of novel methodologies and emerging directions as deep learning methodologies continually evolve. Moreover, in practical application, novel issues and trajectories perpetually manifest, demanding a comprehensive exegesis. To rectify this lacuna, this article amalgamates current research trends and avant-garde methodologies while systematizing the utilization of anterior deep learning techniques. The evolution and extant status of deep learning in diagnosing faults for rotary machinery were delineated, with the intent of providing orientation for prospective research. Over the bygone decade, archetypal deep learning theory has empowered the diagnosis of faults in rotating machinery by directly establishing the nexus between mechanical data and fault conditions. In recent years, meta learning methods aimed at solving small sample scenarios and large model transformers aimed at mining big data features have both received widespread attention and development in the field of fault diagnosis of rotating machinery equipment. Although excellent results have been achieved in these two directions, there is no review and summary article yet, so it is necessary to update the review literature in the field of fault diagnosis of rotating machinery equipment. Lastly, predicated on a survey of the literature and the current developmental landscape, the challenges and prospective orientations of deep learning in rotary machinery fault diagnosis are presented.

Section: Ganmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

“…With the incredible development of deep learning in recent years, intelligent diagnosis is also effective making it af research hotspot [31][32][33]. Han et al [34] proposed a semisupervised fault diagnosis approach specifically designed for wind turbines, aiming to train unlabeled samples effectively. Wang et al [35] input the TFR results processed by dislocations into the CNN network.…”

Section: Introductionmentioning

confidence: 99%

A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions

Hu,

Li,

Yang

et al. 2024

Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed in this paper. This method determines the optimal search starting point using the time-frequency Gini coefficient and extracts the time-varying gearbox meshing frequency components from the time-frequency representation (TFR) results. Furthermore, TLOT method is utilized to process the strongly time-varying gearbox signal. In the order domain, multiple frequency components are extracted to reconstruct signal. Simulation and experimental results demonstrate that the proposed method achieves a superior ridge extraction effect on gearbox experimental signals. It accurately converts unstable signals into angular domain stable signals, enhancing the energy aggregation of time-varying unstable signals in the order domain. This approach addresses the problem of weak harmonics component extraction from gearbox signals and effectively reduces interference in the resonance band. So that realized the fault diagnosis of the gearbox under unstable working condition.

“…Inspired by adversarial learning (AL) [8], Han et al [9] introduced AL as regularization into CNNs, and proposed a deep adversarial CNN to avoid overfitting to smallscale annotated samples. In addition, a semi-supervised deep neural network by integrating AL and metric learning is established, which can achieve high-precision fault diagnosis of wind turbines with limited annotated samples [10]. Similarly, Lei et al [11] focused on few-shot fault diagnosis under variable operating conditions and utilized OT for data augmentation, thereby proposing a meta-transfer learning framework embedded with prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

An improved tracking method for bearing characteristic frequencies in the time-frequency representation of vibration signal

Chen,

Qi,

Yun

et al. 2024

Rolling bearing is one of the most critical components for support and energy conversion in machines. The fault characteristic frequency of time-frequency representation has received increasing attention in bearing diagnosis under variable speed conditions. However, the FCFs-extracted methods have poor adaptability to amplitude attenuation and noise interference for the reason of local distortions or even transitions in the estimated instantaneous frequency ridges. Consequently, this paper proposes an improved FCFs tracking method for variable speed bearing diagnosis. In which, a strategy for locating distortion intervals is first developed using exponential smoothing and residual distribution. Subsequently, an advanced fast path optimization method, including peak map renewal and curve search optimization, is proposed to extract the interested ridges. Finally, the probability density function of curve-to-curve ratios is designed for accurately identifying bearing fault. The simulation and experiment results demonstrate the effectiveness of the proposed method.