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The Data Scientist

Predictive maintenance

How Data Science Can Aid in the Predictive Maintenance of Forklifts

Maintaining forklifts is no small task, especially when your company aims to minimise downtime and reduce operational costs. Relying on traditional maintenance strategies like fixed schedules or reactive repairs often means that you might need to replace parts before replacement actually needed, or rely on breakdowns and disruptions as a prompt for springing into action. Such approaches can be inefficient and costly, especially when you’re working with a larger forklift fleet.

The use of data science takes the guesswork out of forklift maintenance. By leveraging the power of advanced analytics, your organisation can shift to a predictive maintenance approach. This ensures that every unit in the fleet remains in optimal condition while saving the company time and money. 

What Is Predictive Maintenance?

Predictive maintenance uses data-driven insights to anticipate when a piece of equipment is likely to fail or require servicing. Instead of following a fixed maintenance schedule, you can rely on real-time data and predictive algorithms to determine the most effective times to repair units or replace forklift parts. This proactive approach significantly reduces downtime and costs by allowing you to address potential issues before they can escalate. 

With a strong foundation in data science, predictive maintenance offers powerful capabilities to revolutionise equipment management. The following are just some of the key advantages and applications:

1. Collecting the Right Data

Data collection serves as the foundation of predictive maintenance. Modern forklifts are often equipped with sensors that track various performance metrics and the condition of their components. By focusing on specific data points, you can gain valuable insights into the health of the equipment. 

These metrics are often used to determine the best time to carry out forklift maintenance, repairs, and part replacements:

  • Engine Temperature – Monitoring engine temperature helps you identify overheating issues before they cause serious damage. Abnormally high temperatures, for instance, can indicate problems such as coolant leaks or excessive wear on engine components.
  • Hydraulic Pressure – Hydraulic systems are critical for the operation of forklifts. Pressure fluctuations can signal potential leaks or blockages that can contribute to sudden failures. 
  • Battery Voltage – Excellent battery health is essential in ensuring the proper operation of electric forklifts. Regularly measuring battery voltage helps you detect declining battery performance or charging issues. This, in turn, lets you preemptively schedule battery replacements or servicing.
  • Usage Hours – Keeping track of operating hours provides a clear picture of wear and tear on your forklifts. This data can inform maintenance schedules and help your maintenance team plan servicing based on actual usage rather than arbitrary time intervals.
  • Vibration Levels – Excessive vibrations can indicate underlying mechanical issues, such as misaligned components or worn bearings. Monitoring vibration levels allows you to identify and resolve these problems before they worsen.

Collecting and analysing these data points lets you create a comprehensive picture of your forklifts’ performance and health, forming the basis for effective predictive maintenance. 

2. Analysing Data for Predictive Insights

Once you’ve collected data from your forklifts, you need to analyse it using data science techniques. Below are key methods of analysis, and each offers unique insights that your company can use to optimise its fleet maintenance strategy:

3. Failure Pattern Recognition

Machine learning algorithms can process historical data to identify patterns that precede equipment failure. Let’s say that an increase in vibration levels consistently occurs before a forklift’s motor fails, the algorithm can recognise this and send you an alert. This, in turn, lets you stage a prompt intervention that can prevent equipment failure or unsafe operations. 

4. Predictive Modelling

By taking various factors such as usage patterns, environmental conditions, and historical performance data into consideration, predictive models can estimate the remaining useful life (RUL) of critical components. For instance, a model might predict when a hydraulic pump is likely to fail based on its operating hours and pressure fluctuations. This allows you to replace or repair components just in time, avoiding unnecessary costs while ensuring reliability.

5. Maintenance Schedule Optimisation

Analysing data on usage hours, wear rates, and other performance metrics can help you develop customised maintenance schedules tailored to each forklift in your fleet. You can be certain, then, that servicing occurs only when necessary, thus reducing equipment downtime without incurring unnecessary costs for your company. Armed with this information, you can ensure that the forklifts that are used more intensively can undergo more frequent maintenance, while less-used units can follow a lighter schedule.

Applying these analytical techniques allows you to transform raw data into actionable insights, enabling a predictive maintenance strategy that maximises efficiency and minimises disruptions.

Reaping the Benefits of Predictive Maintenance

Powered by data science, predictive maintenance offers numerous advantages for businesses like yours that rely on forklift fleets. The ability to address potential issues before they lead to failures is key to reducing downtime, lowering operational costs, and improving equipment reliability. It also enhances safety, as well-maintained forklifts are less likely to experience accidents caused by mechanical failures. Furthermore, by focusing on data-driven maintenance strategies, you foster a culture of innovation and efficiency within your organisation, which better positions your business as a leader in fleet management.