A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

Abstract

Nowadays, cold ironing technology has been demonstrated to be an effective solution to deal with the environmental and social problems brought by port ship emissions and relevant effects. The working states of cold ironing equipment, especially the key components such as circuit breakers, transformers and frequency converters, have a significant effect on the safety and reliability of the whole system. However, due to the harsh working environment of cold ironing equipment, they are prone to a high risk of failure. In this respect, fault diagnosis methods can play a significant role in detecting potential failure in time and guarantee the safe and reliable operation of the cold ironing system. In recent years, research on the fault diagnosis of a cold ironing system has been rapidly growing, and this paper aims to present a comprehensive review of this literature, with an emphasis on the fault diagnosis technology applied to the key components in a cold ironing system. This review classifies the literature according to the type of key component, and, for each special type of component, the fault diagnosis methods are further categorized and analyzed. This paper provides useful references for professionals and researchers working on the fault diagnosis of a cold ironing system and points out valuable research directions in the future.

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

“…Without data, the sensor is in an idle state. Each type of data needs compatible microcontroller and Xbee RF module settings (baud rate, parity, start bits, end bits, and data packet) [5]. Figure 13 illustrates how the XBee component was configured to operate at 9600 baud using X-CTU software in order to function as either a coordinator or an end terminal.…”

“…Nevertheless, there are still engineers in the industry who are griping about consistent, regular, and unforeseen motor faults. Nevertheless, there are some faults that are difficult to identify in their early stages, and the symptoms of these faults only become apparent as they accelerate the component of induction motors ageing [4,5].…”

“…MCSA is a simpler, more effective method for diagnosing induction motor faults [4][5][6]. This technique estimates the existence of pre-recorded fault signatures using a pattern recognition processing information to the strong electric signal of motor drives in order to make the determination [11].…”