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Automated fault detection and diagnosis for supermarkets-method selection, replication, and applicability

机译：超市的自动故障检测和诊断 - 方法选择，复制和适用性

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摘要

Automated fault detection and diagnosis (AFDD) can highlight system faults that otherwise go unnoticed. Supermarkets have strong potential to benefit from AFDD because substantial energy and environmental impacts can be avoided by diagnosing faults in the refrigeration, air-conditioning, and lighting systems. Many methods have been proposed in scientific literature and patents, but adoption is not widespread. This paper describes a project in which an extensive review was conducted of existing AFDD methods, and two disparate methods were selected for further study. The methods include a rule-based method and a data driven method. Each method was tested and analyzed using curated measurement data from existing supermarkets with and without faults present. The rule-based approach is most effective when the AFDD performance index is a controlled variable. The data driven method can detect changes in energy consumption, but is not as effective when input variable have significant fluctuation during unfaulted operation. (C) 2019 Elsevier B.V. All rights reserved.
机译：自动故障检测和诊断（AFDD）可以突出显示否则不会被注意的系统故障。超市具有强烈的潜力，可以从AFDD中受益，因为通过诊断制冷，空调和照明系统中的故障，可以避免大量能量和环境影响。在科学文献和专利中提出了许多方法，但采用并不普遍。本文介绍了一个项目，其中对现有的AFDD方法进行了广泛的审查，并选择了两个不同的方法进行进一步研究。该方法包括基于规则的方法和数据驱动方法。通过来自现有超市的策划测量数据进行测试和分析，并使用存在的超市进行测试和分析。当AFDD性能索引是受控变量时，基于规则的方法最有效。数据驱动方法可以检测能量消耗的变化，但是当输入变量在未引用的操作期间具有显着波动时，不如当前的波动。 （c）2019 Elsevier B.v.保留所有权利。

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