Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey <s>  <s> On-line monitoring and diagnosis of transformers have been investigated and discussed significantly in last decade. This study has concentrated on issues arising while on-line transformer winding deformation diagnosis is going to be applied on transformers with various kinds of techniques. From technical perspective, before replacing off-line methods by on-line methods and eventually by intelligent approaches, practical challenges must be addressed and overcome. Hence, available off-line transformer winding deformation diagnosis methods are discussed precisely. Mathematical calculation in on-line short circuit impedance measurement is investigated. On-line transformer transfer function measurement setup is presented. A profound insight to the problems pertaining on-line transformer winding deformation recognition methods, characterizes existing online methods, explains the concepts behind online measurements and striving to open the discussion doors towards challenges are discussed. In the end a 400 MVA step up transformer has been taken as a case in order to clarify the capability of Frequency Response Analysis (FRA) method in fault detection while short circuit impedance could only demonstrate some rough understanding about transformer condition. <s> BIB001 </s> Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey <s>  <s> Electrical System Preventive and Predictive Maintenance methodologies have experienced growth due to new available technologies. This paper will review the historical and current approaches to electrical equipment maintenance, and also provide available technologies which support the future modes of equipment maintenance. Low Voltage and Medium Voltage systems will be reviewed. The need for improved reliability and uptime continues and effective monitoring systems support a new concept of Zero-Planned Outages. Monitoring parameters linked to potential failure modes allows for two key needs to be addressed: first, to notify end-users of a pending failure condition; and second, to recommend maintenance and/or inspections based on a trending analysis of failure mode contributing parameters rather than planned outages. This paper shall review both IEEE and end-user surveyed failure indicators and technologies to continuously monitor and alarm on such failure indicators. <s> BIB002
but in other areas as well, e.g. medicine: there are more and more systems that provide continuous monitoring of single human organs and, consequently, early detection of diseases. There are many reasons for this: on the one hand, rapid development of technology enables continuous data monitoring, big data storage, faster, more complex and more reliable data analysis; development of complex monitoring systems and their interconnection allows the formation of data clusters which describe certain physical phenomena as a result of mechanical, thermal, electromagnetic or other types of processes inside or outside the observed object. On the other hand, sensors are getting smaller and less power intensive, e.g. the development of sensors based on microelectro-mechanical systems (MEMS sensors). Furthermore, easier and faster access to stored data enables an intelligent approach to condition assessment because it can now be based on previously gained experiences and allows the application of artificial intelligence or machine learning. In doing so, obtained data can be grouped in the appropriate clusters based on the same object design characteristics which leads to the formation of databases that can be used in experimental frameworks for further development of diagnostic methods. Thus far, this approach has been proven to be reasonable and justified. The requirements for improved system uptime, reliability and availability of the future smart grid calls for the shift from time-based or condition-based maintenance towards the maintenance based on continuous online monitoring of the equipment operating conditions BIB002 . Increased requirements also point to the need of the ability of such monitoring solutions to inform about potential failure even before it happens as well as provide ongoing maintenance recommendations. An excellent paper on transition from offline to online transformer winding deformation diagnostics is one from Bagheri et al. BIB001 , where they classify winding deformations, give an overview of both offline and online methods and then address practical problems of their implementation. A continuous online monitoring system has to meet several goals. One of the most important goals is that the applied methods should not interfere with the normal operation of the monitored object. Also, they should not require any special mode of operation. Many published papers on this subject (and this seems like a trend in the modern diagnostic approach) are about the analysis of acoustic signals. This is quite logical because the collection of such data sets generally represents a non-invasive method. One can state that the most sensitive part of a power transformer is the on-load tap changer (OLTC). This conclusion stems from several surveys carried out on the national and international level that studied the reliability of power transformers. First survey conducted by CIGRÉ published in 1983  analysed data from 13 countries. Data has shown that about 41% of failures were due to the OLTC (Fig. 1) . Given that the OLTC contacts are the only movable part of the transformer and that every mechanical movement is accompanied by occurrences such as friction which cause wear & tear and ultimately damage to the contacts, it is no FIGURE 1. Power transformer failure location analysis from 1983 report  and 2015 report  . wonder that OLTC is a very common cause of failure. Besides OLTC, common causes of failures on power transformers are short circuits and deformations of its windings. According to the aforementioned study, core, windings and OLTC failures constitute 63% of all observed failures. Some newer compiled failure statistics  (transformer failure data from 22 countries) show progress in the reliability of the OLTC (27%), but there is still a high percentage of failures connected to windings (40%) and core (3.4%) (see Fig. 1 ). This is precisely the reason why this paper focuses on vibro-acoustic diagnostic methods for these three parts of the transformer. Precisely speaking, vibro-acoustic methods should also include acoustic methods related to partial discharge (PD) detection. Partial discharge causes occasional breakdowns in electrical insulation. PD activity causes several physical effects including optical (light), thermal, chemical, electromagnetic and other phenomena. Such breakdowns in turn also cause the appearance of the acoustic waves that can be monitored by appropriate sensors placed on the tank of the transformer. However, issues related to PD are beyond the scope of this paper. Rest of the paper is structured as follows: in section II a brief overview of causes behind transformer vibrations is given; in section III, diagnostic data collection and preprocessing are discussed; sections IV and V are the focus of the work and here the literature on vibro-acoustic methods for condition assessment of power transformers is surveyed. In section VI, the surveyed literature is summarised in a table format and taxonomy is provided. Section VII concludes this paper. In the end, in Section VIII, we provide a glimpse towards future.