「英中对照」Limble发布《维护状况报告》调查 称美国设施团队预防性维护完成率仅54%

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Dive Brief

Teams complete just 54% of scheduled preventive maintenance, while 47% of maintenance leaders report more than 10 hours of unplanned downtime in a typical month, according to a new survey.一项新调查显示，团队仅完成了54%的计划内预防性维护，而47%的维护负责人表示，通常一个月内会有超过10小时的计划外停机时间。

Published Oct. 9, 2026 发布于 Oct. 9, 2026

First published on首次发布于

Dive Brief:

• Missed scheduled preventive maintenance can lead to downtime costs of $100,000 or more, according to a Limble survey of 686 U.S. maintenance and operations professionals.根据一项针对686名美国维护与运营专业人员的Limble 调查，未按计划完成预防性维护可能导致 $100,000 或以上的停机成本。
• In addition, 65% of survey respondents said that reactive, as opposed to preventive, maintenance increases risks to technicians.此外，65%的调查受访者表示，被动维护而非预防性维护会增加技术人员面临的风险。
• These findings can be attributed in part to “limited time for hands-on work, expertise that may never get documented, and inconsistent use of maintenance data as teams work to prevent failures and move beyond reactive maintenance,” Limble said in a news release.这些结果的部分原因在于，“各团队在努力预防故障并摆脱被动维护时，用于实操工作的时间有限、专业知识可能从未被记录，以及维护数据的使用不一致，”Limble 表示。

Dive Insight:

The survey comes as manufacturers are increasingly turning to artificial intelligence and automation to improve various parts of their operations, including predictive maintenance. 调查开展之际，制造商正越来越多地转向人工智能与自动化，以改善运营的各个环节，包括预测性维护。 

According to the survey, more than half of maintenance teams said they were using or piloting AI tools to help them do their jobs, and 92% agreed that AI needs “good, complete maintenance data” to work properly. However, the survey found that few manufacturers actually follow a data management plan in its entirety, which can result in maintenance issues that could have been prevented.调查显示， 超过一半的维护团队称其正在使用或试点使用 AI 工具来辅助工作，92%的受访者认同 AI 需要“良好、完整的维护数据”才能正常运行。然而，调查发现，很少有制造商真正完整遵循数据管理计划，这可能导致本可预防的维护问题。

“Most maintenance teams aren't short on technology. They're short on time and information they can trust," Limble CEO Gary Specter said in a statement. "Adding AI won't automatically solve the problems they face every day. We have to make it easier for the technician to capture what they know and give teams information they can use to act before a failure happens.”“大多数维护团队并不缺技术。他们缺的是时间和可以信赖的信息，”Limble 首席执行官 Gary Specter 在一份声明中表示。“引入 AI 并不会自动解决他们每天面临的问题。我们必须让技术人员更容易记录自己掌握的知识，并向团队提供可用于在故障发生前采取行动的信息。”

“Teams have to fix how maintenance work gets recorded before AI can do much with it,” Limble said in its State of Maintenance Report, of which the survey was a part. “AI in maintenance runs on the records a team already keeps, making the quality of those records the thing that decides how far it gets.”“团队必须先改进维护工作的记录方式，AI 才能利用这些记录发挥更大作用，”Limble 在其《State of Maintenance Report》中表示，本次调查是该报告的一部分。“维护领域的 AI 依靠团队已保存的记录运行，因此这些记录的质量决定了 AI 能取得多大成效。”

Other key survey findings include:其他关键调查发现包括：

• Although 82% of survey respondents said their organization uses a computerized maintenance management system, only 39% said their entire team uses it regularly, and 71% reported that someone on their team asked for information that their CMMS already contained.尽管82%的调查受访者表示其所在组织使用了计算机化维护管理系统（CMMS），但只有39% 的人表示整个团队都在定期使用该系统，71% 的人表示团队中有人曾索要其 CMMS 中已包含的信息。

• Sixty one percent of respondents said the way they currently approach maintenance increases the risk of unplanned downtime, higher costs and safety issues. Unplanned work is often done under increased time pressure and on equipment that is too hot to handle safely, Limble said.61%的受访者表示，他们目前开展维护的方式增加了计划外停机、成本上升和安全问题的风险。Limble 表示，计划外工作往往在更大的时间压力下进行，而且涉及设备温度过高，无法安全操作。

• Nearly three quarters said they rely on a “critical facility asset” that only one team member knows how to repair, while 47% said they have lost irreplaceable maintenance knowledge due to staff turnover.近四分之三的受访者表示，他们依赖一项“关键设施资产”，而该资产只有一名团队成员知道如何维修；47%的受访者表示，由于员工流失，他们失去了无可替代的维护知识。

• Forty eight percent of respondents said they use preventive rather than predictive maintenance, although the survey’s “work score” pegged that figure at 68%.48%的受访者表示，他们采用预防性维护而非预测性维护，不过该调查的“工作评分”评定该比例为68%。

• Staffing shortages were cited as the single biggest obstacle to better maintenance practices, followed by aging equipment, budget constraints and “too much reactive work.”人员短缺被认为是改善维护实践的最大障碍，其次是设备老化、预算限制和“过多的被动工作”。