Human-machine collaboration in predictive maintenance (PdM)

Predictive maintenance (PdM) (opens in new tab) is a proactive maintenance strategy that uses data analysis tools and techniques to detect anomalies and potential issues in machinery and equipment before they fail and lead to unscheduled downtime. Unlike traditional maintenance approaches—such as reactive maintenance (opens in new tab), which addresses equipment only after it fails, or preventive maintenance (opens in new tab), which relies on fixed schedules—PdM aims to predict and prevent failures through continuous monitoring and analysis.

PdM spans various industries, including manufacturing, energy, transportation, and healthcare. Human-machine collaboration in PdM involves the integration of advanced technologies, such as sensors, machine learning (ML) algorithms, and data analytics, with human expertise and decision-making. This collaboration leverages the strengths of both humans and machines to enhance maintenance processes. Machines handle data collection and analysis, providing insights and predictions, while humans interpret these insights, make informed decisions, and take necessary actions.

In this blog, we’ll explore the collaboration between humans and machines in PdM, its benefits and challenges, and future trends and representations.

The role of machines and humans in PdM

Machines and people have been working together since the great revolution, and they will continue to collaborate across sectors with new and exciting changes. As we discussed previously, machines are advancing to the point where they can self-diagnose (opens in new tab). What does this kind of evolution mean for the relationship between people and machines?

Jason Afara, our Director of Solutions Consultants, shares his thoughts on the machine and human dynamic in PdM:

“Right now, the critical thing to remember in the relationship between human and machine, at least in the context of predictive maintenance, is this … machines collect the data, analyze it, predict problems, and automate tasks, while the person has the job of interpreting the data, making the decisions on what to do with the data that’s been analyzed, implement any changes, and always look to improve,” says Jason.

Below are some common examples of the role of machines in PdM:

• Data collection: Sensors and IoT devices continuously monitor equipment, collecting data on temperature, vibration, and pressure.
• Data analysis: Machine learning (ML) algorithms process and analyze vast amounts of data to identify patterns, anomalies, and potential failure points. This ultimately assists in the decision-making process.
• Predictive analytics: Advanced predictive models forecast equipment failures and maintenance needs based on historical and real-time data.
• Automation: Automated systems can trigger alerts, schedule maintenance tasks, and perform certain maintenance activities without human intervention.

“The role of the machine in PdM is also advancing in how we can react. Previously, if something didn’t sound right with a machine, it would have been flagged and fixed by a technician who had been there for a while, or it would have been seen during a preventative maintenance schedule. Now, we don’t necessarily need to interact with the machine. We get the data right to our fingertips in something like a CMMS, and that data will get to us quicker and with even more detail,” Jason adds.

Jason indirectly references the evolution of the human-machine collaboration, something covered by experts and authors in the manufacturing and automation sector [1]. As machines advance and quicken with the quality and amount of data they can capture, people will evolve to make faster decisions.

Jason highlighted a strong point earlier regarding the role of people in human-machine collaboration that we need to circle back to. “We can’t have successful PdM without people,” Jason adds. People are essential in PdM for tasks that require critical thinking, contextual understanding, and complex decision-making.

The human role in PdM includes:

• Interpretation of data: Engineers and maintenance professionals interpret the insights and predictions generated by machines, considering factors like operational context and business priorities. This task isn’t easy, and different people can look at data differently. It’s important to collaborate with your team and test your thinking.
• Decision-making: Humans decide on the appropriate maintenance actions, balancing cost, safety, and operational impact. They also get support from other departments to gain access to the machine.
• Adaptability: Maintenance professionals bring their expertise, problem-solving skills, and speed when it comes to challenges and managing the rapid evolution of PdM technologies.
• Implementation: Skilled technicians carry out maintenance tasks, repairs, and adjustments that machines are not equipped to handle.
• Continuous improvement: Human expertise is vital for refining PdM processes, incorporating feedback, and enhancing the accuracy of predictive models.

It goes without question that people and machines need to collaborate to execute PdM and enhance maintenance processes. Let’s review a case study that highlights the success of this kind of collaboration.

A practical example of human-machine collaboration in PdM

In 2017, the Massachusetts Bay Transportation Authority (MBTA) commuter rail system experienced the highest number of mechanical failures in the U.S., primarily involving locomotives [2]. In response, the MBTA created a program to upgrade its legacy locomotives, incorporating AI and machine learning to predict maintenance problems before they lead to breakdowns.

Ryan Coholan, the MBTA’s chief railroad officer, recognized the potential of data analytics to improve commuter rail performance. The MBTA previously collected diesel locomotive oil samples and conducted primary analyses, but an approach was needed to relate sample values to maintenance problems. Coholan enlisted Mike Jensen from 4Atmos Predictive Analytics to explore using machine learning models to predict engine breakdowns. The MBTA had extensive historical data from oil samples, which was used to train the model.

The trained machine learning model analyzed elements within the oil to predict engine issues. It could identify high-probability engine problems within the next fifteen days, providing specific predictions like turbocharger or injector failures based on oil composition. The model’s success led to the implementation of regular oil analysis, now conducted every ten days. The project, named Project Velocity, integrated AI-driven oil analysis into the MBTA’s daily operations. The MBTA established an in-house oil lab, enhancing its maintenance capabilities and attracting interest from staff.

Benefits of the MBTA’s PdM model

The predictive maintenance model provided substantial benefits:

1. Cost savings: Predicting and preventing failures before they occurred saved significant time and money.
2. Operational efficiency: Preventing on-track failures avoided passenger inconvenience and improved service reliability. Awareness of data at near real-time, remotely from the machine. Ability to change context based on the available data.
3. Investment and practices: The success in predictive maintenance prompted a $40 million investment in locomotive rehabilitation and changes in maintenance practices.

The program dramatically increased the mean miles between locomotive failures and improved on-time performance for the commuter rail system.

Benefits and challenges of human-machine collaboration

As we’ve seen in the MBTA’s project, human-machine collaboration in PdM can be very effective and beneficial. Still, it is important to highlight the challenges of this kind of collaboration. Below is a table to illustrate some of the benefits and challenges of this collaboration:

By addressing the challenges and capitalizing on the benefits, industries can achieve greater efficiency, safety, and cost-effectiveness in their maintenance practices.

Future trends of human-machine collaboration in PdM

As we reviewed earlier, the future of human-machine collaboration is an exciting topic; when data becomes faster to access, people evolve to respond quickly, too. Some trends shaping the future of PdM include the integration of IoT technologies and Industry 4.0, as well as the adoption of emerging technologies like digital twins.

“IoT devices like sensors are collecting real-time data from equipment, but these devices are going to allow us to monitor data down to the second, not down to the hour, or even several days of data,” says Jason.

It’s not necessarily a psychic prediction to say that the future of IoT devices and Industry 4.0 will involve faster, better, and more responsive data readings on machines. We know from other technological evolutions that product development focuses on things like speed of data transfer and lowering the cost of manufacturing, and therefore, a lower price to purchase said technology. In the later point, reducing the cost means more access, which means more manufacturing facilities can implement smart factories.

A confident prediction is the fusion of physical equipment with digital systems, which enables real-time interaction and autonomous decision-making. Machines equipped with cyber-physical systems (CPS) can self-monitor and communicate their status, optimizing maintenance schedules. Additionally, the use of digital twins more proactively will prove to evaluate the outcomes of various maintenance actions, leading to more informed decision-making. Digital twins and machine learning will likely work with artificial intelligence (AI) to automate anomaly detection, learn even more in-depth about a team’s maintenance process, and evaluate current and ongoing strategies.

Human-machine collaboration in PdM represents a transformative approach

The evolution of human-machine collaboration in PdM reflects a continuous shift towards more proactive, efficient, and intelligent maintenance practices. From the early days of reactive maintenance to the present era of AI-driven predictive maintenance, the collaboration between humans and machines has been instrumental in enhancing equipment reliability, reducing costs, and improving operational efficiency. As technology continues to advance, this collaboration will only deepen, leading to even more innovative and effective maintenance solutions.

References

[1] M. Nardo et al. (2020) The evolution of man–machine interaction: the role of human in Industry 4.0 paradigm. Available: https://www.tandfonline.com/doi/full/10.1080/21693277.2020.1737592 (opens in new tab) (Accessed: July 15, 2024).

[2] T. H. Davenport and S. M. Miller, Working with AI: Real Stories of Human-Machine Collaboration. Cambridge, MA: The MIT Press, 2022.