Predictive Maintenance: Improved Efficiency & Reduced Costs with Machine Learning
Predictive maintenance is a maintenance strategy that utilizes machine learning algorithms to analyze data from sensors, equipment logs, and other sources to predict when a machine is likely to fail. Machine learning algorithms can also help identify patterns and relationships in the data, enabling organizations to make more informed decisions about maintenance schedules and spare parts inventory management.
In the automotive industry, machine learning can be used to improve predictive maintenance by analyzing vast amounts of data from various sources, such as sensors, telematics systems, and maintenance logs. This data can be used to develop predictive models that identify patterns and relationships between various factors and equipment failures. For example, machine learning algorithms can analyze engine vibration and temperature data to predict when a component is likely to fail. Moreover, predictive models can analyze data on driving patterns, road conditions, and fuel consumption to determine the optimal time for maintenance activities such as oil changes and tire rotations.
Machine learning is a powerful tool that can help organizations in the automotive industry optimize their predictive maintenance programs and improve equipment efficiency, reducing costs and increasing overall operational efficiency. Yixin Zhang, Data Scientist at Sigma Technology Insight Solutions
In this case study, we uncover how Sigma Technology Insight Solutions supported the Swedish vehicle manufacturer and contributed to the development of the ML model to understand and predict the lifetime of brake pads that are used in their trucks.
About the client
The client is a Swedish manufacturer of heavy-duty commercial vehicles. The company is known for its commitment to safety, sustainability, and efficiency and offers a wide range of trucks for various applications, including long-haul, construction, and distribution. In addition, the company provides a range of services and solutions to its customers, including maintenance, financing, and telematics solutions.
The challenge: goals of predictive maintenance in the automotive industry
Brake pads are a critical component of a vehicle’s braking system and need to be maintained regularly to ensure their proper functioning. Predicting when brake pads are likely to fail can help prevent unexpected failures and reduce the risk of accidents. The client required a predictive maintenance solution for brake pads to calculate the lifetime of their brake pads. As a result, data-driven decisions would ensure the safety and reliability of vehicles, as well as reduce maintenance costs and increase the efficiency of fleet operations.
Our involvement
The client approached the team with a need for an expert data scientist to assist in the development of a machine-learning model. Yixin Zhang, a highly skilled Data Scientist at Sigma Technology Insight Solutions, joined the effort and provided crucial consulting services. The objective was to create a model that could predict the durability of brake pads based on historical data that included factors such as road quality, vehicle speed, temperature, and others.
However, the team faced a challenge as some of the data was missing, which had to be restored and refilled to ensure the accuracy of the results. To overcome this, the team used advanced data restoration techniques to refill the missing data and ran the data through the model to identify data relations.
The end result of their efforts was an ML model written in Python that predicts the impact of various factors on brake pad durability. This tool will serve as a valuable asset to the client, allowing them to make data-driven decisions and continuously enhance their product offerings.
Further steps
The machine learning solution has the potential to be adapted and expanded to improve the wear resistance and durability of a wide range of spare parts beyond its current application. In addition to this, innovative technology could be leveraged in other industries, such as manufacturing and consumer electronics, to enhance the performance of their products and prolong their lifespan. The scalability of the ML solution makes it a versatile tool that can be applied in multiple contexts, delivering a significant competitive advantage to businesses that adopt it. Furthermore, the solution can be customized to meet the specific requirements of each industry, ensuring that it provides the optimal outcome in each case. The potential of the ML solution to improve the performance and longevity of products in various industries makes it an invaluable tool for organizations seeking to remain at the forefront of their respective fields.
