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Enhancing grain harvester fatigue reliability to support sustainable agriculture: A review of research status and prospects.

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Τίτλος: Enhancing grain harvester fatigue reliability to support sustainable agriculture: A review of research status and prospects.
Συγγραφείς: Tang, Zhong, Wang, Haiyang, Tian, Liquan, Lao, Liyun, Sun, Hongyan
Πηγή: Science Progress; Oct-Dec2025, Vol. 108 Issue 4, p1-35, 35p
Περίληψη: Global sustainable development relies heavily on stable and efficient grain production, where reliable and durable harvesting machinery plays a crucial role. However, the harsh operating conditions experienced by these machines in the field often lead to premature failure. These failures not only cause crop losses and economic burdens but also undermine sustainability goals by increasing the lifecycle environmental footprint through the consumption of materials and energy for repairs and replacements. Furthermore, unexpected downtime can lead to harvest delays, resulting in food waste and inefficient fuel use. This review focuses on research aimed at enhancing the operational fatigue reliability and durability of harvesting machinery. It specifically reviews the latest advancements in the application of sensor technology, signal processing methods, computer simulation techniques, and data analysis methods to advance harvesting machinery durability research. Furthermore, it identifies the challenges in current research, including obtaining accurate load data, handling uncertainties, and validating models. Looking ahead, this review highlights a necessary shift towards integrated, intelligent systems that can transform harvester design and maintenance from a reactive process into a proactive strategy for ensuring lifecycle sustainability. [ABSTRACT FROM AUTHOR]
Copyright of Science Progress is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Enhancing grain harvester fatigue reliability to support sustainable agriculture: A review of research status and prospects.
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  Data: <searchLink fieldCode="AR" term="%22Tang%2C+Zhong%22">Tang, Zhong</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Haiyang%22">Wang, Haiyang</searchLink><br /><searchLink fieldCode="AR" term="%22Tian%2C+Liquan%22">Tian, Liquan</searchLink><br /><searchLink fieldCode="AR" term="%22Lao%2C+Liyun%22">Lao, Liyun</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Hongyan%22">Sun, Hongyan</searchLink>
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  Data: Science Progress; Oct-Dec2025, Vol. 108 Issue 4, p1-35, 35p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Global sustainable development relies heavily on stable and efficient grain production, where reliable and durable harvesting machinery plays a crucial role. However, the harsh operating conditions experienced by these machines in the field often lead to premature failure. These failures not only cause crop losses and economic burdens but also undermine sustainability goals by increasing the lifecycle environmental footprint through the consumption of materials and energy for repairs and replacements. Furthermore, unexpected downtime can lead to harvest delays, resulting in food waste and inefficient fuel use. This review focuses on research aimed at enhancing the operational fatigue reliability and durability of harvesting machinery. It specifically reviews the latest advancements in the application of sensor technology, signal processing methods, computer simulation techniques, and data analysis methods to advance harvesting machinery durability research. Furthermore, it identifies the challenges in current research, including obtaining accurate load data, handling uncertainties, and validating models. Looking ahead, this review highlights a necessary shift towards integrated, intelligent systems that can transform harvester design and maintenance from a reactive process into a proactive strategy for ensuring lifecycle sustainability. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Science Progress is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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