To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e., the “fault symptoms”). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g., past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets.

Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway switches are required. A common approach to track superstructure monitoring is to measure the acceleration caused by vehicle track interaction. Local interruptions in the wheel–rail contact, caused for example by local defects or track discontinuities, appear in the data as transient impact events. In this paper, such transient events are investigated in an experimental setup of a railway switch with track-side acceleration sensors, using frequency and waveform analysis. The aim is to understand if and how the origins of these impact events can be distinguished in the data of this experiment, and what the implications for condition monitoring of local track discontinuities and defects with wayside acceleration sensors are in practice. For the same experimental configuration, individual impact events are shown to be reproducible in waveform and frequency content. Nevertheless, with this track-side sensor setup, the different types of track discontinuities and defects (squats, joints, crossing) could not be clearly distinguished using characteristic frequencies or waveforms. Other factors, such as the location of impact event origin relative to the sensor, are shown to have a much stronger influence. The experimental data suggest that filtering the data to narrow frequency bands around certain natural track frequencies could be beneficial for impact event detection in practice, but differentiating between individual impact event origins requires broadband signals. A multi-sensor setup with time-synchronized acceleration sensors distributed over the switch is recommended.

Digitization and artificial intelligence for monitoring the health conditions of switches and point machines in the railway infrastructure has become increasingly important. By using a large unlabeled dataset of motor current curve measurements collected over two years from 58 electro-mechanical point machines in the Netherlands—containing over 1.7 million samples—this paper introduces an unsupervised health indicator. In contrast to many existing approaches, the indicator is comparable across point machines and operating temperatures. It also features transparent training and prediction pipelines, and its outputs are easily interpretable by maintenance personnel. The resulting ranking can be used to quickly gain an overview and prioritize planned maintenance between machines. Evaluation of the health indicator on the large dataset demonstrates behaviors that highlight the importance of condition monitoring systems that go beyond the analysis of individual current curves. Instead, short-, mid-, and long-term observations should be synthesized into a coherent overall health assessment.

Condition monitoring is an important tool in the maintenance of railway infrastructure, and data-driven approaches are gaining in popularity. Switches are complex systems that include both complex track superstructure—which is by design subject to increased dynamic load compared to open track—and point machines that must operate in concert to achieve optimal performance. Existing condition monitoring methods are often limited to a specific type of measurement data to monitor one of these subsystems of a switch and only certain faults. Research and industry are now moving toward integrating all available information into a single, unified system, enabling a clearer understanding of the interactions between subsystems, more efficient maintenance, and better fault root cause diagnosis. This paper investigates the relationships between motor current curves of electric point machines, track geometry data collected by measurement trains, and maintenance logs for 58 switches in the Netherlands over almost 3.5 years. The aim is to provide a foundation for the design of such holistic monitoring systems as well to derive insights and requirements for future data gathering. The analysis reveals that maintenance actions are the dominant factor influencing the measurement data. Major track geometric modifications appear to act as persistent boundary conditions for point machine operation. Given that switches require more frequent maintenance than open track and point machines exhibit dynamic behavior, continuous track geometry monitoring during operation is essential for accurate data analysis and maintenance assessment.