Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods.

中文翻译：

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基于 AutoML 的预测框架，用于吸附冷却和海水淡化系统的预测分析

与使用接近环境温度的低品位废热的其他系统相比，吸附冷却和海水淡化系统具有明显的优势。由于提高其性能（包括可靠性和故障预测）具有挑战性，因此开发高效的诊断系统具有重要的现实意义。本文介绍了人工智能（AI）和自动化机器学习方法（AutoML）在现有吸附冷却和海水淡化设施的计算诊断系统的实际应用中的应用。总共 1769 个包含指示故障状态的数据的模拟数据点被应用于开发一个全面的基于 AI 的诊断 (AID) 系统，涵盖广泛的 42 个输入参数。本文介绍了一种吸附式冷却和海水淡化系统的状态监测系统。本研究的新颖性主要体现在两个方面。首先，智能系统使用 42 个不同操作参数的广泛输入数据集来预测复杂的三床吸附式制冷机装置中各个组件的健康或故障状态。开发的 AID 专家工具基于从 DataRobot 平台生成的 42 个模型中选择最佳模型，并在复杂的现有三床吸附式制冷机上进行了验证。AID 系统正确识别了各种安装组件的健康和故障状态。所开发的专家系统在预测紧急状态方面非常有效（AUC = 0.988，RMSE = 0.20，LogLoss = 0.14）。所提出的方法构成了一种快速简便的故障预测技术，并且与其他状态监测方法相比是一种补充工具。