Original Article Detection of structural damage and estimation of reliability using a multidimensional monitoring approach JO Ortiz 1 , German R Betancur 1 , J Go ´ mez 1,2 , Leonel F Castan ˜ eda 1 , G Zaj¸ ac 3 and RE Gutie ´ rrez-Carvajal 1 Abstract Many structural elements are exposed to load conditions that are difficult to model during the design phase, such as environmental uncertainties, random impacts, and overloading, amongst others, thus increasing unprogrammed main- tenance and reducing confidence in the reliability of the structure in question. One way to deal with this problem is to monitor the structural condition of the element. This approach requires supervising several signals coming from critical locations and then performing an accurate condition estimation of the element in question based on the data collected. This study implements a method to diagnose and evaluate the reliability of the bolster beam structure of the railway vehicle during a fatigue test. The results show that multidimensional monitoring not only diagnoses the element accur- ately but also results in correct estimation of reliability. Keywords RAMS, railway vehicle, reliability, sensor fusion, strain measurement, superstructure Date received: 5 May 2016; accepted: 23 March 2017 Introduction Nowadays, structural health monitoring (SHM) is a topic of active research. 1 It consists of the entire pro- cess of signal acquisition, signal processing, and com- puter decision support to plan maintenance activities of critical elements, in order to increase reliability, availability, maintainability, and security indexes (RAMS). 2 SHM deals with real operational condi- tions, many of which are difficult to model during the design stage, such as environmental effects, the alteration of load conditions during operation, struc- tural fatigue, and random impacts, amongst others. 3,4 This type of maintenance strategy allows one to esti- mate the best time to perform a maintenance task, thus providing the availability of qualified operators, tools for the task and so forth, thereby overall improving the system to which the structure belongs. 5,6 Railway vehicles are designed to operate for at least 30 years, but real-life conditions present a com- plex mixture of alternated loads specific to each rail- way system, 7 such as railway routes, vehicle types, different operational schedules, and maintenance pro- grams amongst others. This could result in early wear- ing of components, such as cracks and fissures, which requires frequent corrective maintenance tasks and reduces the lifetime of a vehicle, in terms of operation and reliability. 8,9 These scenarios get worse if one takes into account that the effort of SHM on railway vehicle structures is limited to the bogie, leaving the maintenance program of the remaining elements mainly supported by nondestructive tests, avoiding real-time signal measurements that would otherwise diagnose the inline structure. 10 One of these compo- nents is the bolster beam, which is the main structure of the carbody and is not a disposable piece. The diagnosis of a bolster beam includes a set of nondes- tructive tests, such as those that include magnetic par- ticles, ultrasound, permeable liquids, amongst others that would require the whole train to be taken out of service, thus decreasing its availability. On the other hand, regarding fissures, the repair action would be to hire an expert operator to weld the fissure, whose level of expertise would depend on the location and type of weld. 11,12 Hence, it is critical for the maintenance 1 GEMI Research Group, EAFIT University, Medellı ´n, Colombia 2 Instituto Tecnolo ´gico Metropolitano, Medellı ´n, Colombia 3 Faculty of Mechanical Engineering, Cracow University of Technology, Krako ´w, Poland Corresponding author: RE Gutie ´rrez-Carvajal, GEMI Research Group, EAFIT University, Carrera 49, No. 7, Sur-50, Office 20-133, Medellı ´n, Colombia. Email: rgutier7@eafit.edu.co Proc IMechE Part F: J Rail and Rapid Transit 0(0) 1–12 ! IMechE 2017 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0954409717707122 journals.sagepub.com/home/pif