Going Smart with Predictive Maintenance: Asset Management in Railways

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Abstract

As one of the most asset-intensive industries globally, the rail industry invests nearly 20% of its revenue in asset maintenance to ensure safe operations. What’s more, significant challenges such as spiralling maintenance costs, narrow maintenance windows, non–availability of talent for remote condition monitoring, and heightened customer expectations, plague the industry. Therefore, railways are moving from the traditional scheduled maintenance strategies to asset condition-based predictive maintenance to enhance utilization, reduce costs, and safeguard the durability of their railway networks.

Predictive maintenance involves deploying sensor-based diagnostic monitoring in real-time for assets and sub-systems that provide enough time to act – based on the recorded condition parameters. It is important to understand the various ways through which railways can use next-gen digital technologies such as remote sensors, edge computing, IoT platforms and Big Data analytics to formulate predictive maintenance strategies. Understanding the impact of such strategies in driving signficant improvements in reliability, asset availability cost eficiency, and customer satisfaction also is of greater importance.

Need of Predictive Maintenance Analysis in Railways

In railways equipment failures can result in fatal consequences; unplanned outages and unreliable operations can severely impact revenues as well as customer satisfaction and safety. Time-based traditional maintenance strategies fail to capture the exact state of an asset, and lead to unnecessary maintenance and high operating costs. Hence, needless to say that predictive maintenance is a hot button issue in the rail industry today. The approach allows users to monitor assets continuously, proactively identify probable defects, and initiate necessary maintenance work before an asset fails, enhancing system availability and reducing maintenance costs. Sensors placed along railway tracks or mounted onboard rolling stock help railways remotely capture critical parameters associated with rolling stock as well as fixed infrastructure – in real-time. 

Along with asset-related data, other data like weather and geographical conditions can also be captured to enable superior asset management. The data captured across the network of sensors is fed into a predictive model, enabling proactive maintenance operations to eliminate unplanned downtime of assets.

The benets do not end there. Accurate forecasting of spare parts requirements through predictive analytics helps cut down on their procurement and shipping costs. Accurate insights into manpower requirements also help drive precise maintenance crew scheduling. Predictive maintenance solutions also play a critical part in supplier contract negotiations. They provide relevant data such as warranty information, supplier details, traceability and demand forecasting to help railway companies negotiate the best price, including terms and conditions for the new contract.

Four major approaches of a successful predictive analysis

Ascertaining how can railways develop a successful predictive maintenance strategy following four approaches can be stated to be important:

• Identify data needs: Most current systems in rail organizations are incapable of meeting the data requirements needed for predictive maintenance. One of the first steps is to obtain the data required for the solution development process using a top-down approach. Clearly defining the business objectives allows solution developers to drill down and pinpoint the required data.
• Define the right system requirements: This is the most crucial step in developing a predictive maintenance solution. It is important to define the scope of the solution and identify business-critical needs and parameters for prediction. A wrong system can lead to unsuccessful outcomes and limit user confidence.
• Blend data analytics with domain expertise: While data scientists help develop predictive algorithms, rail domain expertise is essential to guide data scientists in building the right algorithm aligned with specific business needs.
  
• Create an environment for value-addition: The scope of a predictive management solution goes beyond predicting failures to predicting several business scenarios, deploying suitable prescriptive actions and improving maintenance-related performance indicators. These include suggesting the next maintenance activity, just-in-time inventory planning for replacement of parts, identifying systems that need an upgrade in their design due to their continued poor performance, and so on.

Smart Maintenance Strategies: Role of Digital Technologies 

It is clear that adopting relevant emerging technologies such as sensors help achieve new levels of success and efficiency across the rail industry using predictive maintenance strategies. But with the sensor data volumes growing phenomenally over time, data management and analysis needs are growing. To turn this enormous data into actionable insights, major railroad companies are turning to cloud-based IoT and Big Data technologies. 

Big Data analytics can help connect the dots across the entire rail network such as rails, bridges, stations, and so on. It enables the railways to develop predictive algorithms from heterogeneous data sources, real time communications, and scalable data structures which helps in obtaining rapid insights from disparate sources of information to help improve asset availability and service levels, reduce service delays due to unplanned outages, and implement smarter maintenance strategies.

Roadblocks: An Introduction

Despite the promise of predictive maintenance strategies, many rail organizations continue to delay investing in them. The reason seems to be an obvious one – It is difcult to shift from traditional, scheduled maintenance to a predictive model in one leap. Nature of maintenance practices, resources, as well as geographic and weather conditions vary from region to region, as a result of which trains and networks are designed and developed to meet specific requirements – a major barrier to the adoption of predictive maintenance. 

The additional challenges that include may be summarized as under:

1. Developing confidence in the predictions: Rail companies that are in the initial stages of IoT implementation are facing the challenge of ’false alarms’. To mitigate this, along with accurate predictive algorithms, it is necessary to incorporate noise correction methods.
2. Meeting regulatory compliance demands: Safety is paramount to railway transportation, leading to regulatory constraints on the asset maintenance and certification procedures. 
3. Developing the required tool kit and desired skill set: Predictive maintenance requires the deployment of latest technologies, making it difcult to create large scale skill sets in new technologies within the organization. Similarly, it is also important to develop the necessary skills to understand the predictions and historical data and correlate the facts to drive accurate asset status, what-if analysis and improved decisions.
4. Need for large capital outlays: Railways typically deal with a large asset base that operates in harsh operational conditions. Migrating large scale operations from traditional to predictive maintenance demands huge investments in terms of time, money, and technology along with meticulous planning.

Next-gen IoT Platform-based Predictive Maintenance

A software that can predict failures and trigger maintenance workflows and interventions need extensive access to high-quality data from multiple sources such as diagnostic vans, wayside sensors and so on. To produce rapid ROI while transforming the maintenance procedures completely, the solution must be truly flexible, scalable and integrated to meet dynamic requirements.

It is believed that an IoT-based platform with the following capabilities is best-suited to monitor and analyze asset health data for metro & railways:

• Data capture and management: The platform should host a diverse range of sensors to capture asset data from various condition monitoring devices- irrespective of the data format, data source, or the type of sensor. This raw data can be processed at the edge and stored in databases or file systems, depending on the type.
• Analytics and business intelligence: The platform should use the available sensor data along with other relevant data to develop meaningful insights using various analytics tools such as machine learning algorithms for anomaly detection and real-time warnings, predictive asset health assessment, stream analytics and so on. With dashboards based on historic asset condition data and predictive analysis of sensor data, the platform will enable business intelligence and drive superior decision-making.
• Integration with enterprise applications: The platform should seamlessly integrate with enterprise asset management (EAM), data warehouse, and other enterprise IT systems using standard middleware platforms to create a united view and trigger events and workflows for subsequent activities.
• Cloud-enabled: The platform should be deployed on the cloud to allow for scale and adjustments based on changing business requirements.

Summary

Rail customers today demand flawless, reliable, and safe services. For railways catering to these growing expectations requires the deployment of next-gen digital technologies to redefine and re-imagining several organizational processes in addition to upskilling its workforce. As a result, the time is ripe for data analysts, computer professionals, and rail domain experts to collaboratively drive predictive monitoring strategies through digital technologies such as IoT, Big Data technologies, and sophisticated predictive analytics algorithms.

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