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AI Use Cases

A collection of over 250 uses for artificial intelligence

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Predictive maintenance

Introduction

The transport industry is the most vital component of the modern economy as it provides essential services for the supply chain, travelling, and even leisure such as tourism. Still, this sector has a number of problems, including the support of a large park of automobiles and structures. This is where predictive maintenance comes in, a concept enhanced by technologies such as Machine Learning (ML), Internet of Things (IoT) and Artificial Intelligence (AI). Predictive maintenance is the use of these technologies in determining the possible failures of facilities before they actually happen thereby minimizing on time lost in transport systems. This is because predictive maintenance using historical data, sensor information and sophisticated algorithms can be used to identify potential problems ahead of time.

Challenges

There are several issues that the transport industry is facing and predictive maintenance aims at solving them. These include the costs that come with reactive maintenance approaches where problems are solved only when they arise and this may call for expensive repairs and causes prolonged down time. Another challenge is the complexity of the transport systems because it deals with a massive amount of data gathered from various systems. Also, some of the transport systems are located in hard-to-reach areas, which makes routine maintenance a challenge. Last but not the least, safety is a major concern in the transport industry and system failures can be catastrophic.

AI Solutions

There are various challenges that affect the efficiency of the transport system in delivering its services to the public and providing an effective and seamless flow of traffic. AI, ML, and IoT offer smart solutions to these challenges. The smart devices that are incorporated in the transport systems are equipped with features that enable them to collect real time information about the systems. Such data can be used by the ML algorithms to predict potential failures by recognizing the trends and discrepancies. AM can also help in the automation of the maintenance management process including the scheduling of the maintenance activities as well as the optimization of resources. For instance, IBM’s Prescriptive Maintenance on Cloud has incorporated the use of AI and IoT in the identification of asset failures while Microsoft’s Azure AI has provided the transport sector with the necessary tools for predictive maintenance through the implementation of sophisticated cloud-based analytics and machine learning solutions.

Benefits

Predictive maintenance has numerous advantages as outlined below. This enables organisations to mitigate failures before they happen, cut down on downtime and the associated costs of reactive maintenance. It also enables the efficiency and lifespan of transport systems to be optimized through proper maintenance of the systems. The real time information that is supplied by the IoT devices can be helpful in enhancing the performance of the transport systems as well as enable proper decision making on the part of those managing the systems. Furthermore, predictive maintenance improves safety by minimizing the probability of system failure.

Return on Investment

The ROI of predictive maintenance in the transport industry is impressive. A study by Deloitte shows that predictive maintenance can decrease the maintenance expenses by 5-10%, enhance equipment availability by 10-20%, and increase the lifespan of equipment by several years. As for the financial gains, a McKinsey report stated that predictive maintenance can generate cost savings of $200 to $600 billion within the industrial sector. These costs are saved due to less time spent on downtime, reduced costs on repair parts and labor, and higher operational efficiency.

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