Analysis of Point Machines and Monitoring Systems

Analysis of Point Machines and Monitoring Systems: History

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Subjects: Engineering, Mechanical

Contributor:

Xiaoxi Hu

Tao Tang

Lei Tan

Heng Zhang

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Scholars and researchers have attempted to deploy various kinds of sensors on point machines for anomaly detection and/or incipient fault detection using date-driven algorithms. However, challenges arise when deploying condition monitoring and fault detection to trackside point machines in practical applications.

point machines
condition monitoring
inherent features
external impacts
fault detection system

1. Introduction

Many countries and regions favor railway transportation because of its advantages of large volume, fast speed, low price, punctuality, low energy consumption, and small occupation of land [1]. Although there are many kinds of rail transportation systems, covering high-speed rail networks, urban rail transit systems, mag-lev (magnetic levitation) lines, heavy haul railways, and existing railroads, all of them require turnouts to connect multiple lines. More precisely, point machines (known as railway switch machines) are designed to push and pull the switch rails to allow a train on one track to cross over to another.

Clearly, the safe and reliable operation of point machines contributes enormously to the safety and efficiency of rail transportation. Unfortunately, point machines naturally undergo a degradation process as operational wear takes hold. Mechanical transmission components and electrical units in point machines inevitably fail. Furthermore, point machines work alongside railway lines, where the environment is ordinarily harsh. Diverse faults, including common, intermittent, or even unexpected ones, can occasionally occur, and may cause incidents or even accidents. Poor maintenance and missed faults tend to cause system delays or even heavy casualties, e.g., the Potters Bar accident in 2002, which resulted in seven deaths and 76 injuries [2]. Consequently, railway operators have to confront complicated fault detection during regular inspections. Maintenance and repair staff are required to detect faults over time with a missed detection rate of zero. Hence, scholars and engineers have put forth considerable effort to capture the relevant behavioral characteristics by means of different sensors installed on point machines for fault detection, thereby forming a matter of rising concern as well as a research focus.

Scholars engaged in railway signaling have been taking up the related research directions for more than twenty years, and a number of review articles concerning condition monitoring and fault detection have been published. For example, Márquez et al. [3] summarized the different kinds of sensors and fault detection methods used in three typical types of point machines for the actuators of turnout systems, i.e., electro-mechanical, electro-hydraulic, and electro-pneumatic point machines, up to 2009. Hamadache et al. [2] described the fundamentals of point machines and presented a review of existing techniques according to model-based and data-driven methods up to 2019. In addition, they discussed potential opportunities for future studies.

While there is a considerable body of research on point machine fault detection, the majority of previous studies focused solely on implementing data-driven models on collected data and have often neglected to consider the requirements of condition monitoring and fault detection system of point machines, particularly data-driven algorithms.

In order to describe the requirements for a point machine fault detection system, we start with the requirements, inherent features, and external impacts of point machines.

2. Requirements for Point Machines

High safety and reliability. Because point machines drive the turnout, which is the crucial section of railway track, they involves the operational safety of trains. Any hardware device or software installed in point machines should be reliable and trustworthy, including the sensors, supporting signal processing, and monitoring procedures.
Long service life. Point machines are designed and manufactured with a focus on a lengthy service life, as replacing the entire machine that works along the trackside requires time and money. Therefore, providers can guarantee high quality, with suppliers typically claiming more than one million throwing movements before machine overhaul.

3. Inherent Features for Point Machines

Electro-mechanical. A point machine is a typical mechanical or electro-mechanical device; it is often driven by an AC or DC motor, and outputs the displacement of the throw slide with the aid of mechanical drive mechanisms. In addition, its structure is non-redundant, that is to say, each component is indispensable to realization of the point machine’s required functions.
Limited space. The majority of point machines are tailored products. Limited space is a basic attribute, as they need to be convenient for transportation and maintenance. This advantage, however, means that there is limited remaining space inside the point machine, making it challenging to fix and to a certain extent relying on sensing units to determine maintenance needs.
Complexity. The electro-mechanical components make a point machine’s structure complex, comprising many mechanical, electrical and even hydraulic or pneumatic items that can potentially cause diverse fault modes. The include single faults, compound faults, intermittent faults, and NFF (no fault found) failures. Furthermore, the constituent parts can vary quite considerably in terms of their failure probability, and these failure probabilities are normally very low. As a result, gathering all possible fault data is extremely difficult.
Variety. Point machines come in three main types: electro-mechanical, electro-hydraulic, and electro-pneumatic [53,54]. Each type has unique parameters. For example, electro-mechanical point machines focus on throwing force and motor power, while electro-hydraulic ones mainly consider the hydraulic system pressure.

Second, these three types have different failure mode distributions. Electro-mechanical point machines commonly experience wear and transmission component fractures, electro-hydraulic ones are prone to oil leakage, and electro-pneumatic ones often face pneumatic subsystem-related issues.

Furthermore, various subtypes exist within each type to meet specific turnout requirements. These subtypes can vary in their motor type, output force, length and displacement distance of the throw slide detection slide, locking mode, etc. For instance, the China Railway Signal and Communication Corporation produces over forty specific subtypes of the ZDJ9 electro-mechanical point machine; among these, the throwing force range is between 2.5 kN and 4.5 kN, the displacement distance of the throw slide ranges from 80 mm to 220 mm, and the same figure for the detection slide varies from 75 mm to 170 mm. It should be noted that these slight discrepancies need to be taken very seriously.
Individual differences. Even within a subclass, there may be non-negligible differences in point machines due to production errors. More significantly, differences may result from external factors such as action frequency, ambient temperature and humidity, electromagnetic interference, and train impacts with varying speeds. As a result, the switching resistance between individual machines can very. This variation in the duration of point machine movements exists within a specific range. It is important to recognize that every individual point machine has its own unique behavior due to slight individual differences and diverse external impacts.

4. External Impacts for Point Machines

Rolling stock and operational planning. The complete structure and compliant dimensional parameters of the turnout determine the safe and reliable passage of trains. Deviation of the geometry or component damage of a turnout may make the point machine unable to work normally, e.g., rail creeping, alignment of switch rails, and rail wear. Train passage through turnouts can generate significant impact loads, especially during wheel–rail transitions in the switch and crossing zones, resulting in high vertical and horizontal loads [55]. In [56], a numerical investigation reported maximum lateral displacements of up to 5 mm and variations of up to 8 mm in high-speed rail. Table 1 shows the vertical displacement of CRH2 EMU after passing through the turnout at a speed of 250 km/h. In fact, the extent of displacement depends on the condition of the point machine, track, and traffic characteristics such as the speed, axle load, and train formation. Operation plans, including train passing frequencies, influence geometric parameters and turnout frame integrity. Train-related events, encompassing rolling stock and operation plans, provide essential insights into the mechanical system’s stability.
Service environment. Point machines operate in diverse service environments influenced by geographical factors such as location, latitude, longitude, and ocean currents. These environments can range from extreme heat during the day to sharp temperature drops at night. For instance, Chinese railway regulations require point machines to function in temperatures ranging from −40 °C to +70 °C. A prime example are the CTS2 point machines installed on the Qinghai–Tibet Railway, which operate in cold high-altitude areas.

In certain cases, railways traverse challenging environments, such as the Saudi Arabian Railway across desert terrain known for its harsh climate and abrasive sand and wind. Polar regions with heavy snowfall pose challenges for point machines as well. Despite implementing protective measures, extreme climates can accelerate performance degradation. Additionally, point machine adjustments made at night may become inaccurate during the day due to changing conditions.

Table 1. Vertical displacement after CRH2 EMU passing through the turnout at a speed of 250 km/h [57].

China Technical Turnout in Wuhan-Guangzhou Test Section		German Technical Turnout in Wuhan-Guangzhou Test Section		French Technical Turnout in Hefei-Nanjing Railway
Sleeper No.	Vertical Displacement	Sleeper No.	Vertical Displacement	Sleeper No.	Vertical Displacement
10	0.62 mm	−3	0.84 mm	−3	0.33 mm
28	0.76 mm	10	0.46 mm	13	0.37 mm
37	0.65 mm	27	0.99 mm	27	0.1 mm
47	0.44 mm	44	0.96 mm	50	0.56 mm

5. Requirements for Point Machines Condition Monitoring & Fault Detection System

Considering the Requirements (Req.), Inherent Features (IF), and External Impacts (EI) of point machines and the characteristics of data-driven algorithms, we have identified eight requirements for point machine fault detection algorithms. These requirements are essential for the development of an intelligent point machine fault detection system as outlined in Table 2.

Table 2. Requirements for condition monitoring and fault detection systems.

Point Machines			Condition Monitoring & Fault Detection System
Req.	IF	EI	Req.	Difficulty	Suggested Priority Level
1			Trustworthy	Hard	High
1	1		Handling multi-source data	Easy	High
1	2	2	Designing and deploying sensors	Moderate	High
	3, 4		Handling imbalanced data	Moderate	Critical
	3, 4		Handling unseen and complex fault modes	Hard	Medium
	4		Handling part-level fault modes	Moderate	Medium
	4, 5	1, 2	Universality & generalization and robustness	Moderate	Medium
2	5	1, 2	Maintaining fault detection performance over time	Hard	Medium

Req. 1: Trustworthiness. Any software and hardware equipped with point machines should be trustworthy. Data-driven models, while achieving impressive results, pose difficulties in terms of understanding their internal mechanisms, as most data-driven models function as “black box” models. However, commercialization necessitates clear explanations about how the models learn, what knowledge they acquire, their decision-making rationale, and the level of trustworthiness they offer. Hence, it is highly recommended that point machine fault detection systems be built on a trustworthy foundation, including both software and hardware. The most important thing is to ensure the interpretability of AI models and the trustworthiness of their outcomes.
Req. 2: Handling multi-source data. Because a single modality provides incomplete insights into the overall condition of point machines [7], even though the force and the current and power signals can best reflect the point machine’s states [2,3], it is highly suggested that point machine fault detection systems effectively integrate data from various sensors in order to comprehensively monitor the state of point machines in terms of Req. 1 and IF 1 of point machines. In addition, certain special scenarios such as sensor failure and parameter offset need to be considered.
Req. 3: Designing and deploying sensors. Due to the Req. 1, IF 2, and EI 2, reliable, high-accuracy, compact, and interference-free sensors should be favored in point machines, particularly non-intrusive and “plug and play” (easily and quickly interchangeable) types. Thus, it is highly recommended to design suitable sensors and to use a reasonable layout.
Req. 4: Handling imbalanced data. In light of IFs 3 and 4, practical point machine fault detection problems face extremely imbalanced datasets (i.e., with over 99% normal samples and less than 1% abnormal). Furthermore, abnormal data contain a variety of fault types. Because imbalanced datasets are detrimental to model training for data-driven methods [58,59], leading to bad performance on fault detection, this is an urgent and critical requirement.
Req. 5: Handling unseen and complex fault modes. Rethinking IFs 3 and 4 of point machines, there are theoretically a number of different fault types for point machines. It is almost impossible to gather all the fault data, as not all faults occur during real operations, especially for new railway lines without historical data. Despite this, unrecorded or unseen faults can affect the determination of the classification boundary between normal and abnormal data. As a result, it is suggested that the system be able to handle both unseen and complex fault modes, even though this is a difficult task.
Req. 6: Handling part-level fault modes. Considering IF 4 of point machines in combination with the literature survey, electro-pneumatic point machines, which are commonly used in turnout areas and marshaling yards, have received limited attention from researchers. Moreover, scholars have overlooked fault detection for specific parts, such as the retarder, throw rod, and switch circuit controller [60]. However, every part within a point machine is crucial, as all lack redundancy. Previously, researchers have mistakenly taken the current, power, or other condition monitoring parameters as an overall performance indicator for point machines. To enhance precision, there is a need to shift focus towards detecting faults at the part level, such as hydraulic cylinders [61] and bearings [62] under daily loads.
Req. 7: Universality, generalization, and robustness. Based on IFs 4 and 5 and EIs 1 and 2, developing a model with high universality, generalization, and robustness is recommended. More precisely, a highly universal model can operate on different types and models of point machines without the need for individual model training in each case, which reduces the costs of system deployment and maintenance while allowing the model to be used across a wider railway network. Strong generalization capabilities imply that the model performs well even when facing new and previously unseen fault patterns or environmental conditions. In addition, a robust model maintains stable performance when dealing with noise, interference, sensor failures, and changes in environmental conditions. This means that the model can reliably perform fault detection even in complex real-world operating environments, thereby reducing the $F A R$ and $M D R$ .
Req. 8: Maintaining fault detection performance over time. Considering Req. 2, IFs 4 and 5, and EIs 1 and 2, more and more observations (e.g., unanticipated fault modes, numerical accumulations) need to be collected throughout the whole life cycle of a point machine while accounting for the changing service environment and imposed time-dependent operation plans. Hence, it is of great importance to ensure that the fault detection model remains effective over time until it can be replaced with a new one.

This entry is adapted from the peer-reviewed paper 10.3390/act12100391

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