Computer modeling of ship combined propulsion plants

Combined propulsion plants (CPP) are increasingly being used on modern ships of foreign and domestic construction. A feature of such plants is that the energy for the movement of the vessel is generated in them in two (or more) different types of ship engines - heat and electric ones, working on a common propulsor. Combined plants are complex electromechanical systems designed to provide propulsion in various modes of ship operation and generate electricity in a cruising mode or during a lay-up. CPP combine the advantages of traditional propulsion plant with heat main engines and electric propulsion plants. The study of the physical properties and the principle of operation of CPP without a comprehensive study of the object using the model is impossible. Computer models and computer modeling are widely used to study the properties of complex objects. Nowadays, personal digital computers are widely used for computer modeling. Standard packages and programs are used as programs and packages for computer modeling.

Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

Hazard Analysis for Escalator Emergency Braking System via System Safety Analysis Method Based on STAMP

Due to the complex mechanical structure and control process of escalator emergency braking systems (EEBS), traditional hazard analysis based on the event chain model have limitations in exploring component interaction failure in such a complex social-technical system. Therefore, a hazard analysis framework is proposed in this paper for hazard analysis of complex electromechanical systems based on system-theoretic accident model and process (STAMP). Firstly, basic principles of STAMP are introduced and comparison with other hazard analysis methods is conducted, then the safety analysis framework is proposed. Secondly, a study case is performed to identify unsafe control actions of EEBS from control structures, and a specific control diagram is organized to recognize potential example casual scenarios. Next, comparison between fault tree analysis and STAMP for escalator’s overturned accident shows that hazards related to component damaged can be identified by both, while hazards that focus on components interaction can only be identified by STAMP. Besides, single control way and tandem operation process are found to be the obvious causal factors of accidents. Finally, some improvement measures like decibel detection or vibration monitoring of key components are suggested to help the current broken chain detection to trigger the anti-reversal device for a better safe EEBS.