# A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
## 2 Condition-Based Maintenance in Railway Systems  
In opposition to run-to failure or corrective maintenance, in which the goal is to replace the faulty component, preventive maintenance strategies, aims to prevent the failure, by means of condition monitoring and early fault detection. A classification of maintenance strategies is presented in Figure 3.  
Several reviews in CBM are available in the literature [1; 34; 35; 36; 37] A comparison between CBM and Time-based maintenance (TBM) is presented in [38]. The cost-effectiveness of CBM is dependent on some factors, such as condition monitoring quality and required planning time. In [39] a framework for CBM implementation is presented. The framework is based on five different blocks in order to improve the interaction among the different disciplines during CBM design and implementation periods. The framework integrates tools as Failure mode and Effects Analysis (FMEA) and standards as ISO 13374. Standard ISO13374, developed for condition monitoring and diagnostic of machines, presents the following functional modules for CBM implementation: Data Acquisition (DA), Data Manipulation (DM), State Detection (SD), Health Assessment (HA), Prognostics Assessment (PA) and Advisory Generation (AG). An example of each module functionality is shown in Figure 4. In the example presented, in the HA module, not only the diagnosed faults are given, the effects on system performance are presented too, in order to help in the maintenance decision-making [40]. In [41], the application of the standard ISO 13374 to a railway system is proposed. An on-board diagnosis is proposed, being the DA, DM, SD and HA on-board implemented, whereas in a remote diagnosis only DA, DM and SD are on-board implemented and data is sent to maintenance centre for diagnosis, as it is shown in Figure 5. Furthermore, in Figure 6, a distributed supervision is presented, in opposition to centralized supervision where a global diagnosis is done. Thus, in the distributed supervision there are local diagnosis units apart from a global one. On-board equipment is normally limited in data storage capacity. On-board diagnosis allow providing HA and there is no need to store so many variables, just the fault indicators. Moreover, communication from train to remote supervision is bounded by transceivers availability and  
Figure 4: Functional modules of ISO 13374 based on [2]. Figure 5: Remote and on-board diagnosis. Figure 3: Classification of maintenance strategies. communication latency. In the near future, this latency could be lower than 100 ms [42]. Thus, on-board diagnosis is more suitable when real-time FDI methods should be implemented, whereas remote diagnosis can be used for delayed HA, as the data are offline analysed. Data can be sent to remote diagnosis, with a time interval over the aforementioned latency, or at the end of the day, depending on the celerity of the maintenance decision-making to offer. In [43] a proposal of CBM for on-board equipment application in a railway application is presented. A rating criterion for health status is presented, as well as four different levels for health assessment. A transition from preventive to predictive maintenance for railway systems is presented in [44], presenting a solution based on remote diagnosis and prognosis. This transition is one of the key points for the smart maintenance proposal in [45]. Other key points in smart maintenance are asset management, support system by artificial intelligence and integrated database. The asset management aims to plan an optimal maintenance in systems, which deterioration is slow but maintenance work takes long. Support system by artificial intelligence expects to help maintenance engineers in the decision-making. Nowadays, engineers need a long-term expertise in order to be able to take a maintenance decision. Finally, an integrated database is needed in order to have all the data coming from maintenance and diagnosis tools available, as well as the historic data.