The paper considers the use of the well-known House of Quality (HoQ) model as a template for structural modeling of cascade failures occurring in interacting networks. The use of HoQ makes it possible to model not only sudden, but also gradual failures in a network system. A distinctive feature of the HoQ model is that its use allows not only to formalize the procedure for constructing a cascade of failures, but also to single out dynamically emerging and disappearing contours, which may include components of both the same and different networks.

The article discusses the operation of robotic manipulation systems for the most popular tasks in the industry. The article provides an implementation of classical methods for grasping objects using a CAD model of the object, highlights their advantages and disadvantages. As a new solution, it is proposed to use a system based on the Action Chunking with Transformers (ACT) neural network architecture. The article details the use of ACT neural networks, the algorithm for training neural networks and launching them within the framework of technological operations of real production. The paper describes the hardware of the system, which includes the ARM95 Collaborative Manipulator, the RealSense Depth Camera D405 depth camera and the HTC VIVE position tracker. The following technological operations were considered as an experimental part of the work: grasping a box object, grasping a pencil object, painting a part and grinding a surface. The developed system shows that modern technologies, including machine learning methods, help to solve complex technological operations with a high level of productivity.

The article investigates the application of ensemble machine learning methods for diagnosing and forecasting the states of technical and technological objects under conditions of noisy data, nonlinear dependencies, and high-dimensional feature spaces. The relevance of the work is driven by the need to enhance the reliability of industrial systems through minimizing accident risks and optimizing operational processes. Traditional approaches demonstrate insufficient accuracy in complex scenarios, motivating the use of ensemble technologies that combine predictions from multiple models to achieve robust results. The primary focus is on Bagging, Boosting, and Stacking methods, their mathematical foundations, and practical implementation. An experiment was conducted using an ensemble of convolutional neural networks (CNNs) for classifying defects in metal microstructure. The results showed an increase in prediction accuracy with an increasing number of classes compared to a single model, confirming the effectiveness of ensembles in reducing error variance and correcting model bias. The proposed approach demonstrates potential for integration into industrial systems, enhancing diagnostic reliability and operational safety of complex technical systems.