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Wagon bearings play a pivotal role in railway operations. Bearing issues can lead to increased maintenance costs and safety risks. Detecting problems like pitting, spalling, cracks, dents, and excessive wear is essential. Early detection can prevent accidents and reduce maintenance expenses, contributing to a safer and more cost-effective railway system.

Consequences of bearing failure

When wagon bearings fail, the consequences can be severe and far-reaching. Here are some of the potential outcomes of bearing failure:
• Safety Risks: Bearing failure can lead to accidents, derailments, and other safety hazards, endangering both passengers and railway employees.
• Costly Downtime: Bearing failure necessitates unscheduled maintenance, resulting in significant downtime and operational disruptions.
• Increased Maintenance Costs: Replacing damaged bearings and addressing related issues can be expensive, impacting the railway’s budget.
• Loss of Efficiency: Trains with failing bearings may experience reduced speed and efficiency, affecting schedules and customer satisfaction.
• Infrastructure Damage: The stress and heat generated by bearing issues can cause damage to railway tracks and infrastructure.
• Environmental Impact: Derailments and accidents can lead to environmental damage, such as spills of hazardous materials.

Powerail: revolutionizing bearing health monitoring

To address these challenges, modern technology has given rise to innovative solutions. Powerail is an autonomous wireless vibration and temperature IoT sensor powered by a vibration energy harvester. It is strategically placed in close proximity to the bearing, ensuring accurate data collection about bearing and wheel health. The system collects essential vibration and temperature data to support the early detection of potential bearing and wheel failures.

Detecting bearing defects through data analysis

Powerail excels in identifying bearing defects through data analysis. In the absence of defects, vibration data exhibits a smooth, consistent pattern with no discernible peaks or shocks. However, when surface non-uniformities or defects occur, a distinctive pattern in the form of harmonic frequencies emerges.
The system uses these patterns to pinpoint the exact location of the defect and assess its severity, allowing for early detection and action.

Taking action and maintenance planning

Once Powerail identifies a bearing issue, it promptly informs the customer. This enables timely maintenance planning, minimizing unexpected downtime. The system estimates the defect’s size and progression, providing a clear picture of the situation. This data-driven approach ensures that maintenance occurs precisely when needed, reducing unnecessary downtime and operational disruptions.

Benefits and Outcomes of Powerail

The implementation of Powerail is revolutionizing the way wagon bearing issues are detected and addressed, resulting in a range of valuable benefits:
Reduced maintenance costs: By eliminating unnecessary maintenance checks and enabling proactive issue resolution, Powerail significantly reduces maintenance costs, leading to substantial savings for wagon owners.
Minimized downtime: Powerail’s data-driven insights have facilitated timely maintenance planning, effectively minimizing unexpected downtime and contributing to a considerable increase in vehicle uptime.
Enhanced safety: This innovative system has greatly improved safety records by identifying potential hazards early, thus reducing the risk of accidents and enhancing overall safety within railway operations.
The railway industry’s efficiency and safety rely heavily on the health of wagon bearings, and Powerail offers a reliable, cost-effective solution to ensure their well-being.

One of the most critical concerns in the railway industry is the early identification of flatspots on train wheels. Flatspots are localized areas of wear or damage on a wheel’s tread, resulting in a flat surface that can lead to a rough ride, increased vibration, and, if left unaddressed, costly consequences. Detecting flatspots in their early stages is vital, and Powerail, plays a pivotal role in making this possible.

Understanding flatspots

Flat spots occur when a rail vehicle’s wheelset drags along the rail after the wheel/axle comes to a halt. They are typically the result of emergency brake usage or slip and slide conditions, causing the wheels to lock while the train is still in motion. Although flat spots are more common during slippery autumn and winter conditions, they can also be attributed to faulty brakes or wheelset bearings. When left unattended, flatspots can worsen over time, necessitating costly wheelset replacements and causing operational disruptions.

The importance of early detection

Early identification of flatspots on train wheels is paramount for the following reasons:

Safety. Flatspots can undermine the structural integrity of a wheel, posing a significant safety risk. Early detection and intervention can prevent accidents, derailments, and potential harm to passengers and railway personnel.

Operational efficiency. The presence of flatspots can lead to further damage to tracks and rolling stock. It can also result in service delays, negatively impacting the overall efficiency and reliability of railway operations.

Cost savings. Addressing flatspots at an early stage is far more cost-effective than replacing an entire wheelset or undertaking extensive repairs. Early intervention can save both time and resources.

Powerail’s role in early detection

Powerail, an autonomous wireless vibration and temperature IoT sensor, revolutionizes the process of flatspot detection:

Continuous monitoring. Powerail’s sensors are strategically placed near the bearings, continuously monitoring the condition of the wheels. These sensors collect crucial data on vibrations and temperature changes.

Pattern recognition. Through advanced data analysis, Powerail can identify irregular patterns in the collected data that are indicative of flatspots. Flatspots typically generate high-frequency shocks or peaks, which stand out from the normal wheel vibrations.

Alerts. Upon detecting unusual patterns, Powerail generates real-time alerts, promptly notifying railway operators. These alerts provide operators with the necessary information to take immediate action.

Proactive maintenance

Powerail empowers railway operators to proactively schedule maintenance when flatspots are detected in their early stages. This approach ensures passenger safety, reduces operational disruptions, and significantly cuts down on maintenance expenses. By addressing flatspots proactively, railway companies maintain high safety standards, optimize operational efficiency, and enhance the overall passenger experience.

In essence, Powerail’s innovative technology enables railway operators to detect and address flatspots early, mitigating potentially hazardous and costly issues. It ensures a safer and more efficient railway system, delivering benefits for both operators and passengers alike. With early detection through Powerail, the railway industry remains on track for a safer and smoother journey.

In the ever-evolving landscape of freight transport, making the right choice between railways and roads can significantly impact efficiency, cost, and sustainability. While both options have their merits, the advantages of railways over roads are increasingly evident when considering sustainable freight transportation.

Efficiency and Capacity

Railways have long been recognized for their efficiency in moving large quantities of goods over long distances. Trains can carry significantly more cargo than trucks, making them a preferable choice for bulk shipments. This inherent capacity allows for economies of scale, reducing the need for multiple trips and minimizing the associated fuel consumption and emissions. Additionally, rail routes are dedicated and free from the traffic congestion often encountered on roads, leading to more predictable delivery schedules.

Environmental Impact

In the era of heightened environmental awareness, the ecological impact of transportation choices cannot be ignored. Railways offer a greener alternative to road transportation. Trains generate fewer emissions per ton-mile compared to trucks. Their energy efficiency is particularly evident when hauling heavy loads over extended distances. By embracing railways, companies can make substantial contributions to reducing their carbon footprint, aligning with sustainability goals.

Infrastructure and Investment

The existing rail infrastructure is a valuable asset when it comes to cost-effectiveness. Railways are already well-established, requiring less maintenance than roads and experiencing fewer disruptions due to weather conditions. Moreover, the potential for investment and expansion exists, with governments and private entities recognizing the benefits of bolstering rail networks for improved freight transportation.

Collaborative Possibilities

Intermodal transportation, which combines various modes of transport, offers an interesting avenue for sustainable freight movement. Trains can seamlessly connect with trucks, ships, and even planes, creating a comprehensive and flexible supply chain network. This integration not only enhances efficiency but also presents opportunities for reducing the environmental impact of the entire logistics process.

Challenges and Solutions

Of course, challenges exist in the transition towards increased railway freight transport. The ‘last-mile’ problem, where goods need to be transported from the railway hub to the final destination, requires innovative solutions. Local distribution networks and improved connectivity between railways and roads are crucial to overcoming this challenge.

Choosing railways over roads for moving goods represents a conscientious step toward sustainable freight transport. Efficiency, environmental benefits, established infrastructure, and collaborative potential collectively make railways an appealing option. By embracing railways, companies and societies alike can contribute to a more sustainable and resilient future, achieving the delicate balance between economic growth and environmental responsibility.

In the modern railway industry, sensors play a crucial role in ensuring safety, efficiency, and seamless operations. These sensors are integrated into various railway systems to monitor track conditions, detect faults, and provide valuable data for maintenance and operational purposes. However, the widespread usage of batteries to power these sensors has raised concerns regarding environmental impact, maintenance costs, and sustainability. In this article, we will explore the consequences of using batteries in railway sensors and explore alternative solutions that promote a more sustainable and efficient future for the railway industry.

The Battery Conundrum

Railway sensors are often placed in remote or hard-to-reach locations, making wired power connections impractical. As a result, batteries have become the default power source for many of these sensors. While batteries provide the advantage of being self-contained and easily deployed, they come with several challenges and consequences:

1. Environmental Impact: The disposal of battery-powered sensors can lead to environmental pollution, as the improper handling of batteries can release harmful chemicals into the soil and water. This raises concerns about long-term sustainability and the industry’s contribution to climate change.

2. Maintenance Costs: Battery replacement and disposal add to the overall maintenance costs of the railway infrastructure. Frequent battery changes in remote areas can be time-consuming, resource-intensive, and expensive.

3. Limited Lifespan: Batteries have a finite lifespan, leading to periodic replacements. This creates interruptions in sensor functionality and requires careful planning and scheduling for maintenance activities.

4. Safety Risks: In some cases, battery-powered sensors may fail without warning, leading to potential safety risks in critical railway operations.

5. Energy Consumption: Constantly replacing batteries contributes to the overall energy consumption of the railway industry, thereby increasing its carbon footprint.

Alternative Solutions for Sustainable Sensor Technology

To address the drawbacks of battery usage in railway sensors, several alternative solutions have been proposed and are currently being explored by the industry:

1. Solar-Powered Sensors: Solar energy provides a renewable and sustainable solution for powering remote railway sensors. By harnessing sunlight and converting it into electrical energy, solar-powered sensors can operate autonomously without the need for frequent battery replacements. This reduces maintenance costs, environmental impact, and enhances sensor longevity.

2. Energy Harvesting Technologies: Utilizing energy harvesting technologies such as piezoelectric, electromagnetic, or thermoelectric systems, railway sensors can convert ambient vibrations, motion, or temperature differences into usable electrical energy. This approach allows sensors to generate their power, eliminating the need for batteries and enhancing their self-sustainability.

3. Inductive Power Transfer: Inductive power transfer systems can wirelessly charge sensors through electromagnetic fields. This technology enables sensors to draw power from trackside infrastructure or passing trains, ensuring a continuous power supply without the need for batteries.

4. Low-Power Design and Energy Efficiency: Optimizing sensor design to minimize power consumption can extend battery life and reduce the overall energy requirements of the railway system.

As the railway industry continues to embrace digitalization and automation, sensors play an ever more critical role in enhancing safety, efficiency, and decision-making processes. However, the prevalent use of batteries in these sensors has significant consequences in terms of environmental impact, maintenance costs, and energy consumption.

To overcome these challenges, the railway industry must actively seek sustainable solutions such as solar power, energy harvesting technologies, inductive power transfer, advanced energy storage, and energy-efficient design. By adopting these alternatives, the industry can transition towards a greener, more cost-effective, and sustainable future, ensuring smooth operations while minimizing its ecological footprint. Embracing such advancements not only benefits the railway industry but also sets a precedent for sustainable practices across other sectors.

In the realm of railway maintenance, effective monitoring of wagon conditions plays a vital role in ensuring safety, efficiency and reliability. Two prominent approaches employed in this domain are condition-based monitoring (CBM) and predictive-based monitoring (PBM).

Condition-Based Monitoring (CBM)

CBM involves periodic inspections and assessments of wagon conditions based on predefined criteria. Here’s an overview of CBM:

1. Regular Inspections: Wagon components, including wheelsets, axles, bearings, brakes, and couplers are inspected at predetermined intervals or triggered by specific events such as mileage, time or performance thresholds.

2. Inspection Techniques: Various non-destructive testing methods such as visual inspections, ultrasonic testing, magnetic particle testing and eddy current testing are employed to detect defects, wear, cracks or any abnormalities in the wagon components.

3. Maintenance Decision-Making: Inspection results are analyzed and maintenance decisions are made based on predefined criteria or industry standards. Repairs, replacements, or further investigations are carried out as necessary.

Predictive-Based Monitoring (PBM)

PBM utilizes advanced technologies and data analytics to monitor wagon conditions in real-time. Here’s how PBM works:

1. Sensor Integration: Sensors, such as accelerometers, temperature sensors, and strain gauges are installed on critical wagon components. These sensors continuously collect data on parameters such as vibration, temperature, loading conditions and stress levels.

2. Data Analytics: Collected sensor data is processed using machine learning algorithms, statistical models, and anomaly detection techniques. This analysis helps identify patterns, predict abnormalities, and detect potential faults or failures in the wagon components.

3. Proactive Maintenance: PBM enables the implementation of proactive maintenance strategies. Early detection of anomalies allows for timely interventions, reducing the risk of accidents, optimizing maintenance schedules and minimizing downtime.

Comparison of CBM and PBM

1. Monitoring Approach: CBM follows a periodic and scheduled inspection approach, whereas PBM provides continuous real-time monitoring of wagon conditions.

2. Data Collection: CBM relies on manual inspections and tests, while PBM employs sensor-based data collection methods to obtain real-time information on wagon conditions.

3. Data Analysis: CBM requires human intervention for analysis and decision-making based on predefined criteria, while PBM relies on advanced data analytics and algorithms to detect anomalies, predict failures and recommend maintenance actions.

4. Early Fault Detection: PBM has an advantage in early fault detection due to real-time data analysis and predictive capabilities. CBM primarily focuses on identifying visible defects during periodic inspections.

5. Maintenance Cost Optimization: PBM enables proactive maintenance strategies by identifying potential failures in advance. This can help optimize maintenance schedules, reduce emergency repairs, and minimize overall maintenance costs. CBM is reactive and may incur higher costs if unforeseen failures occur between inspections.

6. Implementation Complexity: PBM implementation requires sensor deployment, data infrastructure and advanced analytics capabilities. CBM relies on established inspection protocols and expertise, making it relatively simpler to implement.

Applications and Benefits

1. CBM Applications: Condition-based monitoring is suitable for inspecting visible defects, adhering to industry standards and ensuring compliance with regulatory requirements. It provides a structured approach to identify abnormalities, plan maintenance activities and maintain safety standards.

2. PBM Applications: Predictive-based monitoring is ideal for continuous monitoring of wagon conditions, early fault detection and proactive maintenance strategies. It enables optimized maintenance schedules, reduces the risk of unexpected failures and maximizes wagon availability and reliability.

3. Combined Approach: In practice, a combined approach that integrates both CBM and PBM can be beneficial. Periodic inspections through CBM ensure compliance and adherence to standards, while PBM provides real-time data for predictive maintenance interventions.

Both condition-based monitoring (CBM) and predictive-based monitoring (PBM) offer valuable insights into the condition of railway wagons. CBM focuses on periodic inspections to identify visible defects, while PBM utilizes real-time data analytics to predict and prevent potential failures. The choice between these approaches depends on specific requirements, available resources and desired maintenance strategies. A well-designed monitoring system that combines CBM and PBM can optimize maintenance schedules, enhance safety and improve overall wagon performance and reliability in railway operations.

Efficient logistics and timely transportation are critical for businesses relying on railways to move goods. However, delays in returning railway wagons, known as demurrage, can pose significant challenges and financial burdens for shippers and consignees. This article aims to explore the concept of demurrage in railways, its causes, associated costs, and preventive measures, with a focus on incidents as a leading cause and the potential of IoT sensors, like Powerail, in preventing demurrage incidents.

Understanding Demurrage in Railways

Demurrage is a charge imposed by railway companies when wagons are not returned within the agreed time frame. It aims to incentivize efficient wagon utilization and encourage timely return. Demurrage can occur due to various reasons, including inefficient loading/unloading, inadequate planning, infrastructure limitations, unforeseen circumstances, and incidents with wagons themselves.

Demurrage Caused by Incidents: Impact and Costs

Incidents with wagons, such as flat spots and bearing issues, can significantly contribute to demurrage delays. These incidents can lead to extended downtime, reduced availability, safety concerns, and increased maintenance costs. The financial impact of demurrage depends on factors like the daily or hourly rates set by the railway company and the duration of delay. Additionally, indirect costs associated with disrupted supply chains and customer dissatisfaction can amplify the overall impact.

Preventing Demurrage with IoT Sensors

IoT sensors, offer promising solutions to prevent demurrage incidents and enhance railway efficiency. Here’s how they can help:

Real-time Health Monitoring: IoT sensors equipped with accelerometers, vibration sensors, and other advanced technologies can continuously monitor wagon components, including bearings and wheels. Real-time data on vibrations, temperature, and other parameters enable early detection of potential issues, allowing for proactive maintenance before incidents occur.

Predictive Maintenance: By analyzing the data collected by IoT sensors, predictive maintenance algorithms can identify patterns and trends in wagon health. This enables proactive scheduling of maintenance activities during non-revenue-generating periods, minimizing downtime and preventing incidents that could lead to demurrage.

Condition-based Maintenance: IoT sensors facilitate condition-based maintenance, where wagons are serviced based on their actual condition rather than a fixed schedule. Monitoring key parameters, such as bearing temperature and wheel condition, allows for optimized maintenance intervals. This approach ensures timely intervention when necessary, reducing the likelihood of incidents and subsequent demurrage.

Remote Diagnostics and Repair Planning: IoT sensors enable remote diagnostics, enabling maintenance teams to assess wagon health without physical inspections. This streamlines repair planning by ensuring the availability of necessary resources and parts when wagons arrive for maintenance or repair, minimizing delay and demurrage charges.

Data-driven Decision Making: IoT sensors provide a wealth of data that can be leveraged for data-driven decision making. By analyzing historical maintenance data, identifying recurring issues, and optimizing maintenance procedures, railway companies can continuously improve operations, reduce incidents, and mitigate demurrage costs.

Demurrage in railways poses significant challenges and financial implications for businesses. Incidents with wagons, such as flat spots and bearing issues, contribute to demurrage delays, resulting in increased costs and disruptions. However, the implementation of IoT sensors, like Powerail, offers preventive solutions to mitigate incidents, minimize downtime, and enhance efficiency. Real-time health monitoring, predictive maintenance, condition-based maintenance, remote diagnostics, and data-driven decision making are key features of IoT sensor systems that enable proactive maintenance and optimize wagon utilization. By embracing these technologies, railway companies can significantly reduce demurrage incidents, enhance operations, and achieve cost savings while ensuring the smooth flow of goods in the railway logistics ecosystem.

The railway industry faces significant challenges when it comes to ensuring the safety and efficiency of its operations. Wheel and bearing issues in train wagons can lead to accidents, derailments, and costly disruptions. In this article, we delve into the alarming statistics surrounding incidents caused by wheel and bearing defects and explore how implementing advanced technology can revolutionize the monitoring of wheel and bearing health in the railway fleet.

Alarming Statistics: Incidents Caused by Wheel and Bearing Issues

The frequency of incidents in the railway industry related to wheel and bearing defects is a cause for concern. In 2020 alone, the U.S. Federal Railroad Administration reported a total of 1,866 train accidents, highlighting the urgent need for improved monitoring and maintenance practices. Similarly, the European Union Agency for Railways recorded approximately 1,100 accidents in 2019, with wheel and bearing defects playing a significant role in many of them.

The impact of wheel and bearing issues goes beyond accident numbers. The U.S. National Transportation Safety Board (NTSB) has identified wheel and bearing defects as contributing factors in multiple railway accidents resulting in fatalities and injuries. Additionally, bearing defects caused a significant derailment in Ohio, USA, in 2023, prompting evacuation orders for residents in the village of 5,000 people near the Pennsylvania border.

The economic consequences of incidents caused by wheel and bearing issues are substantial. The Association of American Railroads (AAR) estimates that wheel and bearing failures accounted for around 20% of unplanned train delays in 2020, resulting in significant economic losses. These costs include infrastructure damage, emergency response, legal liabilities, and loss of productivity.

Monitoring Wheel and Bearing Health: Implementing New Technology

To address the challenges posed by wheel and bearing issues, the railway industry can leverage advanced technology to monitor their health effectively. By implementing new monitoring systems, the industry can detect potential defects and take preventive measures, thus reducing accidents and improving operational efficiency.

One key aspect of implementing advanced technology is the use of IoT-based sensors equipped with vibration and temperature monitoring capabilities. These sensors provide real-time insights into the health of wheels and bearings. They can detect early warning signs of defects, such as excessive vibration, abnormal temperature variations, and changes in operating conditions.

Another essential component is predictive analytics. By collecting and analyzing data from sensors, predictive analytics algorithms can identify patterns and trends, enabling proactive maintenance planning. Machine learning algorithms can predict the remaining useful life of wheels and bearings, allowing for timely interventions before failures occur.

Advanced condition monitoring systems also play a crucial role in monitoring wheel and bearing health. These systems continuously monitor the performance of wheels and bearings, alerting maintenance teams to potential issues. They provide detailed reports, performance trends, and actionable insights, supporting effective decision-making and resource allocation.

Implementing advanced monitoring technology offers several benefits to the railway industry:

1. Enhanced Safety. Early detection of wheel and bearing defects minimizes the risk of accidents and derailments, ensuring the safety of passengers, crew members, and infrastructure. Real-time monitoring enables proactive maintenance, reducing the likelihood of catastrophic failures.

2. Improved Reliability and Efficiency. Predictive maintenance practices based on accurate data can optimize maintenance schedules, reducing downtime and maximizing fleet availability. Proactive interventions and targeted repairs enhance the overall reliability and efficiency of railway operations.

3. Cost Savings. By addressing potential issues before they escalate, the costs associated with emergency repairs, infrastructure damage, and unplanned downtime can be significantly reduced. Optimal maintenance practices and extended component lifespan lead to long-term cost savings for railway operators.

The incidents caused by wheel and bearing issues in the railway industry are a pressing concern that demands immediate attention. By implementing advanced monitoring technology, such as IoT-based sensors, predictive analytics, and condition monitoring systems, the industry can revolutionize the way it monitors wheel and bearing health in the fleet.
With real-time insights, proactive maintenance practices, and improved safety measures, the railway industry can mitigate incidents, enhance reliability, and achieve cost savings. It is crucial for stakeholders to embrace these advancements and prioritize the adoption of innovative solutions to ensure a safer and more efficient railway network.

Contact Viezo team for individual offer of advanced IoT solution for your fleet.

#Viezo #Powerail

Energy harvesting, the collection of small amounts of ambient energy from the surrounding environment to power autonomous electronic devices or circuits, is a promising technique that can help produce renewable and clean energy and improve infrastructure sustainability. This technique is used to extract a sample of energy from physical phenomena and the maximum feasible amount of energy. This article introduces the different technologies used to convert the harvested energy from vibrations into electricity in the railway industry.

Energy Harvesting Systems

To explain more about the main principle behind the energy harvesting systems, the energy can be gathered from different sources that are available in the industrial or the environmental surroundings such as natural or artificial light, elevated levels of noise, temperature gradients, mechanical vibration, pipes with air or water fluid. The energy is harvested in the peak time of energy availability. Then, it is being saved and stored in a storage device to be used later, meeting the demand and supply for the daily operation of an electronic system at specified periods. Therefore, the main goal of energy harvesting is to store the power to be used later in needed times.The process of converting the vibrations into electricity demands three elements to be included in the energy harvesting system. First, a harvester is a part responsible for gathering energy from the surrounding environment. Second, a low power management system is responsible for converting the voltage level of the harvested energy to those of standelectronicsonic and power the electronic system. The last element is the storage system to save and store the excess of harvested energy. This process leads to many environmental and economic benefits, including the elimination of the dependency upon batteries, boosting and enhancing the functionality of the device, increasing the lifetime of the device, elimination of supply wires, facilitating the process of installation, lowering the level of environmental waste, and presenting cheaper options at lower costs.

Harvesting Types

There are currently three efficient types of harvesting which are currently commercialized in the market.

Electromagnetic Induction

The first one and the oldest technology is electromagnetic induction, discovered by Michael Faraday and James Clerk Maxwell more than two hundred years ago. However, electromagnetism has only been used to generate electricity since the early 1930s. According to Helios Vocca and Luca Gammaitoni, the base of the electromagnetic harvester is the electromagnetic induction phenomena, which are defined as “the production of a voltage across a conductor when it is exposed to a varying magnetic field.”“The inductive technique is usually realized by coupling a permanent magnet and a solenoid in motion relative to each other. These systems show complementary behavior in terms of frequency bandwidth and optimal load in relation to piezoelectric techniques. They are recommended for low frequencies (2–20Hz), small impedance, and medium-size,” was stated in the 2015 book “Micro Energy Harvesting” from Briand, Yeatman, and Roundy.The magnets and coils used in this technology bring their own advantages, including being reliable, maintenance-free, cost-effective, and configurable. On the other hand, they have many disadvantages, including quite a high price, very complicated manufacturing, and fragility during the chaotic vibrations. Regardless of their disadvantages, those harvesters are the most efficient ones in the market, generating the maximum power output from the vibrations.

This technique is used to extract a sample of energy from physical phenomena and the maximum feasible amount of energy.

Piezoelectric Ceramic

The second technology is piezoelectric ceramic. In the last few decades, piezoelectric materials have played a vital role as a mechanism of energy harvesting as the demand for high-power-density and long-lifespan power sources has become higher. The piezoelectric materials have a crystalline structure that facilitates transforming mechanical strain energy into electrical charge. This structure also plays an important role in converting an applied electrical potential into mechanical strain. The ability of such a structure to transform these types of energies helps in providing the wireless sensor nodes with their needs for electrical energy. This is possible by detecting and extracting the mechanical energy from a specified environment, then converting it into electrical energy.There are many advantages when choosing piezoelectric technology in the process of converting mechanical energy into electrical energy. Advantages include high energy conversion efficiency, its ability to be made on a greatly reduced scale, and simple implementation. However, piezoelectric ceramic technology is probably the most complicated way of generating power from vibrations. First, it is made of very toxic materials. Second, it is fragile and can be easily damaged irreversibly, which is also not good for the railway industries since there are many tough vibrations. Third, it is costly. In addition to that, piezoceramics do not differ a lot from the market within its efficiency level. Therefore, it is likely the least desired way of extracting the energy from vibrations.

Piezoelectric Polymer Technology

The third way to do it is to use the piezoelectric polymer technology or a material called PVDF. A fragile film assembled on the cantilever structure with the tip mass on end moving up and down while installed on the vibrating equipment. This is the newest method of generating electricity from vibrations. And just as electromagnetic induction, it has its own advantages and disadvantages, including lower power output and lifetime limitations. However, offering it all at reasonably lower prices, much greater durability of acceleration shocks, and assembly using only environmentally friendly materials.

Our road and skyways have become overcrowded with vehicles, which has created the need to shift to another transportation solution: railways. This shift has played a major role in pushing the railway industry to improve infrastructure and maintenance processes. Consequently, about 25 to 35 percent of total train operating expenses are for track maintenance needs. What is needed are cost effective rail solutions.

The need for solutions that can optimize the work in the railway industry has gained much more importance. These solutions will help detect defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance or overhaul, knowledge of the damage and severity is beneficial, resulting in fewer operational problems, optimizing the fleet availability, and reducing the overall losses and interruption expenses.

Mechanical Sensors
One of the solutions implemented is mechanical sensors. These sensors were applied to measure track geometry parameters. The electromagnetic technique was one of the primary tools for inspecting rail internal defects in highspeed networks until 1953 when ultrasonic transducers were introduced to railway inspection. Since then, various inspection methods have been used for monitoring the health of railway infrastructure or as a preventive measure against rail failures.

Rail axle bearings and wheels are essential parts of the train; any defect might result in severe consequences. Premature failure of rail axle bearings causes a significant increase in train operating costs and can impact train safety. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial vibration and noise levels. Similarly, wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate.

Wheel defects of railway vehicles have a direct impact in causing an increase in attrition and damage to the railway infrastructure. Consequently, this adds additional costs to maintenance and repair, leading to a reduced lifetime and availability of rolling stock. Early detection of wheel defects on trains plays a major role in providing the train operators with timely information on necessary repairs that can prevent further deterioration of the wheels and further damages to the railway infrastructure.

There was an increased focus on the quality of the measurement technologies used to support decision-making within the railway domain with the help of the data-driven railway in the last few decades. Traditionally speaking, a combination of high precision inspection against set standards or, where inspection is impractical, periodic action was used to support safety-based decisions within the railway. While safety is still the main priority, maintenance decisions have gained increased attention. Condition monitoring systems are designed to identify the condition of an asset and inform decision-making processes. Consequently, this helps reduce the incidence of urgent and costly unplanned interventions leading to improvements in performance and safety.

Sensing Technologies
Different sensing technologies monitor axle box bearings and wheels, such as vibration/acceleration, microphone/sound measurement, acoustic emissions/ultrasound, and thermal. Vibration, sound measurement, and ultrasound technologies are used to detect early-stage defects. On the other hand, Thermal sensors are based on detecting the heat generated by severely defected bearings and wheels.

Demand for even more cost-effective and environmentally friendly provided solutions was arisen by railway operators. This led to many technologies integrated into the sensors themselves as a way of cutting extra expenses. New sensors in the market, for example, are based on the vibration energy harvesting technology within the piezoelectric PVDF material, which is environmentally friendly and low-cost. This technology is integrated with the sensor’s design utilizing Piezoelectric PVDF material to convert vibrations into electricity. This technology is based on the cantilever structure, and as the cantilever vibrates, it generates electricity stored in a supercapacitor. Afterward, the electricity from the capacitor is passed to the sensor.

In conclusion, the current market is placing specific demands on the railway industry, resulting in increased needs for cost-effective and safety-oriented solutions. Rail axle bearings and wheels sensors play a major role in detecting early defects that may cause severe consequences. Energy harvesting technology plays its part in saving money and the environment as well.