Designing a predictive maintenance solution for Industry 4.0 represents a paradigm shift in how companies approach maintenance and operations. The evolution towards proactive prevention of operational challenges, through the use of advanced predictive maintenance technologies, is a critical aspect of this new industrial era. These solutions not only help in capturing new revenue streams and saving operational costs but also play a significant role in preventing outages and production downtime. Although machine learning has traditionally posed the largest challenge, the advent of cloud-based solutions for analyzing predictive maintenance data, coupled with increased data analysis competence, has shifted the primary design challenge towards capturing the right set of data and rolling out hardware into a very distributed environment with many security and network limitations. This shift necessitates a comprehensive design process, optimized into four distinct phases, to develop a global, cost-efficient solution with high robustness and security.
Part 1: Initial Data Capture
The first phase focuses on capturing data from a single machine and associated data sources, such as energy consumption, to create a comprehensive dataset for analysis. It proves that relevant data can be captured and forwarded with reasonable costs. Utilizing IoT device management solutions, a machine can be immediately connected to a device, with additional sensors equipped as needed. Linux-based hardware with cellular data connectivity is recommended for minimal interaction with operational technology (OT) network management. The key performance indicators (KPIs) in this phase revolve around the ability to capture relevant data points, such as vibrations, noise, current consumption, or pressure. The aim is to assess whether the relevant physical data can be measured with sufficient accuracy and time resolution, if software can be updated frequently, and the initial data gathering and forwarding solutions can be established. The data analysts can already start to visualize and train the cloud based predictive maintenance models, but probably the data set from one machine is not large enough to be conclusive for this. A successful completion of this phase, confirmed by product management, paves the way to kick off phase 2. The success here does not show yet if the project is successful, it proves that the data can be captured.
Phase 2: Field Testing and Data Expansion
The second phase expands the scope to include more machines, often requiring field tests with a significant number of devices to ensure the AI and machine learning algorithms can achieve the necessary accuracy and confidence intervals. Sometimes the size of the machine park needs to be large enough to actually capture and classify real failures or operational anomalies. This phase enables the data analysts to setup the machine learning models and work on the training. This expansion is facilitated by deploying the software developed in phase 1 across a distributed fleet, leveraging solutions like qbee.io to ensure seamless configuration and installation across any number of devices. In this process a final hardware is selected that fulfills robustness and price criteria. The focus shifts to tuning and expanding the machine learning models, with KPIs centered around achieving the necessary confidence interval for predictions. This is an interactive process that mandates frequent OTA software updates on all devices, preferably connected to a CI/CD pipeline for very quick iteration times across the whole fleet. With fleet management and a good (and independent) connectivity solution such as cellular this is easy to achieve. At the end of this phase product management can review the results and decide if the accuracy derived from the optimized training models is good enough to turn this into a new commercial service.
Phase 3: Product Rollout
Upon achieving a successful prediction rate in the field test, the system is ready to be rolled out as a product. With over-the-air (OTA) updates enabled from day one, solutions like qbee.io facilitate easy full image A/B updates as required. This phase marks the transition of the project to operations, where new revenue streams and business models are created and implemented. It is often underestimated how much work and time this needs. But by introducing device fleet management throughout the design process this works flawlessly and is just an extension of phase 1 and 2. Even if hardware needs to be exchanged due to price or availability this will not be a huge delay. In this phase additional customer requirements might be discovered and through the flexible software update mechanism this can be incorporated into the system.
Phase 4: Lifecycle Management
The final phase emphasizes the importance of lifecycle management, ensuring the system remains secure, online, and updated for many years. Given the longevity expected from industrial applications, efficient fleet management through qbee.io and software updates via CI/CD pipelines are critical. This stage is designed to maintain high service level agreements (SLAs) and quality, thereby preventing costly machine downtime and failures over many years.
Conclusion
In conclusion, designing a predictive maintenance solution for Industry 4.0 involves a comprehensive, phased approach that shifts the focus from traditional challenges in machine learning to capturing and utilizing the right set of data effectively. By systematically progressing through initial data capture, field testing, product rollout, and lifecycle management, companies can develop a robust, secure, and cost-efficient predictive maintenance solution with a very fast time to market. Using the described steps above it is also possible to define clear abortion criteria of the project should the data quality (accuracy or time resolution) or the prediction accuracy be too low. Implementing predictive maintenance not only enhances operational efficiency but also significantly reduces downtime and operational costs, marking a significant leap forward in the industrial sector’s evolution towards smarter, more proactive maintenance strategies. In addition, it opens up for new business models and recurring revenue streams.
So start exploring your predictive maintenance journey today. The team here at qbee.io is happy to help you with the device management part such that you can fully focus on your domain expertise.
