In recent decades, hybridization of superior attributes of few algorithms was proposed to aid in covering more areas of complex application as well as improves performance. Condition monitoring is a major component in predictive maintenance which monitors the condition and identifies significant changes in the machinery parameter to perform early detection and prevent equipment defects that could cause unplanned downtime or incur unnecessary expenditures. An effective condition monitoring model is helpful to reduce the frequency of unexpected breakdown incidents and thus, facilitates in maintenance. ANN has shown effective in various condition monitoring and fault detection applications. ANN is popular due to its capability of identifying the complex nonlinear relationships among features in a large dataset and hence, it can perform with an accurate prediction. However, a drawback is that the performance of ANN is sensitive to the parameters (i.e., number of hidden neurons and the initial values of connection weights) in its architecture where the settings of these parameters are subject to tuning on a trial-and-error basis. Hence, a wide range of studies have been focused on determining the optimal weight values of ANN models and the number of hidden neurons. In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. This paper presents an intelligent system integrating a Radial Basis Function Network with Dynamic Decay Adjustment (RBFN-DDA) with a Harmony Search (HS) to perform condition monitoring in industrial processes. RBFN-DDA performs incremental learning wherein its structure expands by adding new hidden units to include new information. As such, its training can reach stability in a shorter time compared to the gradient-descent based methods. To achieve optimal RBFN-DDA performance, HS is proposed to optimize the center and the width of each hidden unit in a trained RBFN. By integrating with the HS algorithm, the proposed metaheuristic neural network (RBFN-DDA-HS) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA by 2.2% up to 22.5% in two benchmarks datasets, which are numerical records from a bearing and steel plate system and a condition-monitoring system in a power plant (i.e., the circulating water (CW) system). The results also show that the proposed RBFN-DDA-HS is compatible, if not better than, the classification performances of other state-of-the-art machine learning methods.

近几十年来，提出了几种算法的优越属性的混合，以帮助覆盖更多复杂应用领域并提高性能。状态监测是预测性维护的一个主要组成部分，它监测状态并识别机械参数的重大变化，以执行早期检测并防止可能导致计划外停机或产生不必要支出的设备缺陷。有效的状态监测模型有助于减少意外故障发生的频率，从而便于维护。ANN 已在各种状态监测和故障检测应用中显示出有效的效果。ANN 很受欢迎，因为它能够识别大型数据集中特征之间复杂的非线性关系，因此，它可以执行准确的预测。然而，一个缺点是 ANN 的性能对其架构中的参数（即隐藏神经元的数量和连接权重的初始值）敏感，其中这些参数的设置受试错法调整基础。因此，广泛的研究集中在确定 ANN 模型的最佳权重值和隐藏神经元的数量上。在这项研究工作中，动机是开发一种基于自适应 ANN 和元启发式算法混合的自主学习模型，用于优化 ANN 参数，以便网络能够以更灵活的方式执行学习和适应，并更多地处理条件分类任务。在电力系统等行业中准确无误。本文提出了一种智能系统，将具有动态衰减调整的径向基函数网络 (RBFN-DDA) 与谐波搜索 (HS) 集成在一起，以在工业过程中执行状态监测。RBFN-DDA 执行增量学习，其中通过添加新的隐藏单元来扩展其结构以包含新信息。因此，与基于梯度下降的方法相比，它的训练可以在更短的时间内达到稳定。为了获得最佳的 RBFN-DDA 性能，提出了 HS 来优化训练后的 RBFN 中每个隐藏单元的中心和宽度。通过与HS算法集成，所提出的元启发式神经网络（RBFN-DDA-HS）可以优化RBFN-DDA参数，在两个基准数据集中将原始RBFN-DDA的分类性能提高2.2%至22.5%，它们是来自电厂轴承和钢板系统和状态监测系统（即循环水（CW）系统）的数值记录。结果还表明，所提出的 RBFN-DDA-HS 与其他最先进的机器学习方法的分类性能兼容，甚至更好。