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With continued global market growth and an increasingly competitive environment, manufacturing industry is facing challenges and desires to seek continuous improvement. This effect is forcing manufacturers to squeeze every asset for maximum value and thereby calls for high equipment effectiveness, and at the same time flexible and resilient manufacturing systems. Maintenance operations are essential to modern manufacturing systems in terms of minimizing unplanned down time, assuring product quality, reducing customer dissatisfaction, and maintaining advantages and competitiveness edge in the market. It has a long history that manufacturers struggle to find balanced maintenance strategies without significantly compromising system reliability or productivity. Intelligent Maintenance Systems are designed to provide decision support tools to optimize maintenance operations. Intelligent prognostic and health management tools are imperative to identify effective, reliable, and cost-saving maintenance strategies to ensure consistent production with minimized unplanned downtime. This article aims to present a comprehensive review of the recent efforts and advances in prominent methods for maintenance in manufacturing industries over the last decades, identifying the existing research challenges, and outlining directions for future research.

Gearboxes are critical transmission components in the drivetrain of wind turbine, which have a dominant failure rate and the highest downtime loss in all wind turbine subsystems. However, load variations of wind turbine gearbox are far from smooth and usually nondeterministic, which result in inconsistent data distributions. To solve the problem, a novel performance degradation assessment and prognosis method based on maximum mean discrepancy is proposed to test the difference between data distributions and extract the characteristics of multi-source working conditions data. Besides, the increase in sensors will bring more difficulties to establish prediction models in real-world scenarios due to different installation locations. In view of this, a transfer learning strategy called joint distribution adaptation is utilized to adapt data distribution between multi-sensor signals. Nevertheless, the presence of background noise of wind turbine signals restricts the applicability of these algorithms in practice. To further reduce the distribution difference, a novel criterion is proposed to evaluate and measure the data distribution difference between known and tested working conditions based on the witness function of maximum mean discrepancy. The application and superiority of proposed methodology are validated using a wind turbine gearbox life-cycle test data set. Meanwhile, model comparison and cross-verification are conducted between conventional and proposed prediction models. The results indicate that the proposed method has a better performance in performance degradation assessment for wind turbine gearbox.

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