Enhancing Data Accuracy for Predictive Maintenance: Five Strategies
In the world of predictive maintenance (PdM), the collaboration between artificial intelligence (AI) and human expertise is revolutionising the way industries operate. This symbiotic relationship, which combines the precision of AI algorithms with hands-on human experience, is proving instrumental in enhancing data analysis and improving maintenance processes.
At the heart of this transformation is Illia Smoliienko, the Chief Software Officer at Waites. Smoliienko and his team have developed a tool that seamlessly integrates equipment signal processing, spectral analysis, and task management into a single system. This powerful tool is designed to aid analysts in their daily tasks, providing them with a tool that organizes tasks, highlights priorities, and even detects deviations from the norm.
The quality of PdM depends on maintaining a strong data culture at all levels. In many industries, the most effective analysts are those with hands-on field experience. This is why Waites hires vibration analysts who previously worked as maintenance engineers or vibration diagnosticians in industrial settings. A two-tier analysis system is used, where junior analysts first filter out anomalous signals from the data set, while experienced specialists perform in-depth analysis and draw conclusions. This approach reduces the number of routine tasks for senior experts and allows juniors to learn from real cases.
Modern algorithms can identify defect types with an accuracy of 90% to 97% in some cases. However, the accuracy of AI in vibration diagnostics still depends on response speed and human expertise. Human operators can query AI systems in natural language, explore alternative solutions, and validate AI recommendations based on their operational experience and domain knowledge.
In predictive maintenance, AI is used to optimise processes that can't be scaled manually, such as analysing and filtering signals that indicate defects. This collaboration results in enhanced decision making, as AI analyses vast sensor and IoT data to detect patterns and forecast equipment failures before they occur, providing technicians with data-driven maintenance scenarios and projections.
The continuous improvement loop is another key benefit of this human-AI partnership. Feedback from maintenance technicians on AI predictions refines AI models over time, increasing their reliability and effectiveness. Meanwhile, humans upskill by working alongside intelligent systems.
Human planners use AI insights to efficiently schedule maintenance activities, balance equipment availability, and coordinate with broader operational and sustainability goals, such as energy management. This optimised resource use leads to increased confidence and adoption of AI, as it is positioned as a supportive tool that augments rather than replaces human expertise, fostering trust in AI and driving broader organisational acceptance.
The synergy between AI precision and hands-on human experience transforms predictive maintenance from reactive to proactive. This shift reduces downtime, lowers costs, and improves operational efficiency through a continuously evolving human-AI collaboration. In the industrial sector, PdM powered by artificial intelligence is proving to be a game-changer, with real-time data analytics increasingly being used in various industries, including finance, energy, and the industrial sector.
Creating a culture of questions is essential for a team's growth, with a clear rule that it's okay to ask when one doesn't understand. Internal mentoring is another effective way to boost a team's skills, as colleagues can share expertise and automation tips. Conducting retrospectives after errors occur helps identify their causes and prevent similar issues in the future.
A distributed team working across different time zones is beneficial for industries where analysts' work impacts safety or critical operational processes. This setup ensures that there is always someone monitoring equipment condition and performing vibration analysis, helping prevent major breakdowns.
In conclusion, the combination of AI precision and hands-on human experience is transforming the landscape of predictive maintenance. This partnership leverages the strengths of both, providing a powerful tool for industries to predict and prevent equipment failures, reduce downtime, and improve operational efficiency.
Illia Smoliienko, the Chief Software Officer at Waites, is instrumental in the development of a tool that integrates equipment signal processing, spectral analysis, and task management for predictive maintenance analysts. This tool organizes tasks, highlights priorities, and even detects deviations from the norm.
In the industrial sector, the effectiveness of AI in vibration diagnostics still relies on response speed and human expertise. Human operators can query AI systems in natural language, explore alternative solutions, and validate recommendations based on their operational experience and domain knowledge.
The synergy between AI precision and hands-on human experience is transforming predictive maintenance from reactive to proactive, benefiting various industries including finance, energy, and the industrial sector. A distributed team working across different time zones ensures continuous monitoring of equipment condition and vibration analysis, helping industries prevent major breakdowns.
