Abstract In the presence of low-quality industrial process data, generic step response identification methods typically show unsatisfactory performance and heavily rely on manual intervention of technical personnel. This erects obvious obstacles for the advancement of intelligent manufacturing in process industries. To address these challenges, we propose a novel rank-constrained optimization approach to low-order system identification from step response data, which yields much more accurate and robust estimates than existing modeling methods. By exploiting the inherent low-rank structure of the Hankel matrix of ideal step response, parameters of a low-order process can be accurately recovered by solving a rank-constrained program, which effectively bypasses the two-step procedure in some state-of-the-art algorithms involving significant error accumulation. The alternating direction method of multipliers is adopted to effectively solve the nonconvex error minimization problem and circumvent poor local optima. Case studies on both numerical examples and industrial datasets demonstrate that, the proposed method not only gives much better modeling accuracy, but also secures reliable and robust estimates even for raw low-quality industrial data. This is particularly helpful for automated execution of the identification routine without human intervention, with success percentage over 99% that is remarkably higher than the state-of-the-art.

中文翻译：

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基于秩约束优化的低质量阶跃响应数据的高效低阶系统识别

摘要 在存在低质量工业过程数据的情况下，通用阶跃响应识别方法通常表现出不令人满意的性能，并且严重依赖技术人员的人工干预。这为流程工业智能制造的推进设置了明显的障碍。为了应对这些挑战，我们提出了一种新的秩约束优化方法，用于从阶跃响应数据中识别低阶系统，该方法比现有建模方法产生更准确和稳健的估计。通过利用理想阶跃响应的汉克尔矩阵固有的低秩结构，可以通过求解秩约束程序来准确地恢复低阶过程的参数，它有效地绕过了一些涉及显着错误累积的最先进算法中的两步程序。采用乘法器交替方向法，有效解决非凸误差最小化问题，避免局部最优。数值例子和工业数据集的案例研究表明，所提出的方法不仅提供了更好的建模精度，而且即使对于原始的低质量工业数据也能保证可靠和稳健的估计。这对于在没有人工干预的情况下自动执行识别程序特别有帮助，成功率超过 99%，明显高于最先进的技术。采用乘法器交替方向法，有效解决非凸误差最小化问题，避免局部最优。数值例子和工业数据集的案例研究表明，所提出的方法不仅提供了更好的建模精度，而且即使对于原始的低质量工业数据也能保证可靠和稳健的估计。这对于在没有人工干预的情况下自动执行识别程序特别有帮助，成功率超过 99%，明显高于最先进的技术。采用乘法器交替方向法，有效解决非凸误差最小化问题，避免局部最优。数值例子和工业数据集的案例研究表明，所提出的方法不仅提供了更好的建模精度，而且即使对于原始的低质量工业数据也能保证可靠和稳健的估计。这对于在没有人工干预的情况下自动执行识别程序特别有帮助，成功率超过 99%，明显高于最先进的技术。