Application of improved bubble entropy and machine learning in the adaptive diagnosis of rotating machinery faults

Gong, Jiancheng; Yang, Xiaoqiang; Qian, Kun; Chen, Zhaoyi; Han, Tao

doi:10.1016/j.aej.2023.08.006

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Rotating machinery finds extensive applications across various industries, including wind turbines, gas turbines, aviation engines, and more [1,2]. While these large-scale rotating * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

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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.

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Section: Introductionmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

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An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

Hussien,

Bouaouda,

Alzaqebah

et al. 2024

Artif Intell Rev

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A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for a wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from the behavior of Gorilla Troops in the natural world. To emulate the impact of gorillas at each stage of the search process, the GTO employs a flexible weighting mechanism rooted in its concept. Its exceptional qualities, including its independence from derivatives, lack of parameters, user-friendliness, adaptability, and simplicity, have resulted in its rapid adoption for addressing various optimization challenges. This review is dedicated to the examination and discussion of the foundational research that forms the basis of the GTO. It delves into the evolution of this algorithm, drawing insights from 112 research studies that highlight its effectiveness. Additionally, it explores proposed enhancements to the GTO’s behavior, with a specific focus on aligning the geometry of the search area with real-world optimization problems. The review also introduces the GTO solver, providing details about its identification and organization, and demonstrates its application in various optimization scenarios. Furthermore, it provides a critical assessment of the convergence behavior while addressing the primary limitation of the GTO. In conclusion, this review summarizes the key findings of the study and suggests potential avenues for future advancements and adaptations related to the GTO.

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