Predictive maintenance is a key component of the Industrial Internet of Things (IIoT), shifting from reactive fixes and fixed schedules to proactive, intelligent, data-driven strategies that enhance operational reliability. It helps businesses reduce unplanned downtime, extend equipment life, and optimize maintenance cycles.
Although the cloud-based system improved sensor data analysis, it also introduced latency issues, making it less suitable for time-sensitive environments. Traditional automation tools have fallen short due to their rules-based, non-adaptive nature and real-time data processing or inability to respond to dynamic industrial conditions. In contrast, the AI-especially on the edge, enables real-time decisions, reduces delays, mitigates production losses, and significantly improves system scalability and operational accountability.
In this article, Nayangiri Goswami, Manager – Digital Engineering, provides some insights on designing AI-powered predictive maintenance methods for edge environments. He shows how federated learning, trusted safe-fail architecture, and lightweight AI Models can enable large deployments across extensive industrial environments.
Que: How does AI at the edge outperform traditional cloud-based predictive maintenance systems in terms of latency and real-time response?
Ans: Traditional cloud-based predictive maintenance systems rely on transmitting sensor data from machines to a centralized cloud server. This process introduces inherent delays, as data must traverse a network, undergo remote processing, and subsequently receive a response. In numerous industrial environments, particularly those with critical machinery or limited connectivity, these delays can be excessively long for effective action.
Edge AI processes data locally on the edge and resolves it, where it is generated - on machines or on nearby equipment. This local calculation eliminates round-trip delays for clouds, able to detect real-time discrepancy and make rapid decisions.
For example, if a vibration sensor on the motor detects unusual patterns, an edge AI model can immediately flag it down as a possible impact failure and trigger a shutdown or an alert, in real time. In contrast, a cloud-based system can detect the same issue in a few seconds, resulting in damage or unplanned downtime.
In short, bringing AI closer to edge devices significantly improves response times, reduces reliance on constant connectivity, and enables faster, more reliable maintenance decisions.
Que: What are the key considerations when designing AI models for edge devices with limited compute and memory resources?
Ans: While developing AI for edge devices, you need to strike the best balance between performance and efficiency since edge environments have imposed restrictions on things like lower processing power, memory, and energy storage. Here is what should be factored into building these models:
1. Optimizing AI for Edge: Lightweight and Low-Latency Models: Edge devices often have limited power and compute capacity, making it critical to deploy lightweight AI models that are both efficient and responsive. Large models, though powerful, are typically too heavy for edge deployment. Instead, models like TinyML and MobileNet are preferred.
These can be further optimized using techniques such as knowledge distillation, quantization, and pruning to reduce computational load without significantly impacting accuracy.
Additionally, edge applications in safety-critical environments such as predictive maintenance demand near real-time responses. Meeting these latency requirements means prioritizing inference speed over model complexity, ensuring fast and efficient decision-making at the edge.
2. Achieving Power Efficiency: Many edge devices are powered by batteries, and they come with power constraints. Therefore, you need to design your model with power consumption in mind. There are dedicated hardware accelerators for edge workloads, such as NVIDIA Jetson, Google Coral, and ARM-based NPUs, which run AI workloads without draining the battery.
3. Overcoming Connectivity Constraints: Not every industrial site has stable or continuous internet access. That’s why AI models must operate offline, ensuring real-time decisions, maintaining safety protocols, and preventing production halts during connectivity loss. These models should function autonomously and only sync essential data to the cloud when connectivity is available or required.
4. Robustness and Accuracy: Even when working with small models, they must be accurate enough to be of value. This means going through the process of training, tuning, and re-training on industry datasets so that the models can reach acceptable accuracy when put to the test in real world industrial use.
Que: How can federated learning be practically implemented across distributed industrial sites while maintaining model accuracy and data privacy?
Ans: Federated Learning (FL) is a useful way to train artificial intelligence models across multiple edge or local devices in different locations, even if you would not prefer to share the raw data or dataset requirements in a centralized environment.
FL is particularly beneficial for industries with privacy concerns, compliance obligations and other bandwidth limitations that restrict cloud-based training opportunities.
Let’s see how it works in practice:
1. Local Training at Each Site: Each site trains its local version of the model based on its own sensor and operational data. This is done completely on-site, as the data never leaves the site. Rather, the only thing sent will be the trained model parameters (not even the raw data).
2. Secure Aggregation: A central server (which can also be a decentralized network) collects all the local model updates, such as weight or gradient changes, and aggregates them to improve the global model. Techniques like secure aggregation and differential privacy help ensure that no private data can be reverse-engineered from these updates.
3. Periodic Synchronization: The updated global model is then sent back to all the sites so that the learning connections can continue. This cycle repeats so that the model improves over time with all the models at sites learn connections between one another (without risking privacy or putting a strain on the network).
Handling Data and Environment Diversity: Because each industrial context is different (different machines, different workflows, and different environmental conditions), FL connected frameworks must be flexible. To support this flexibility, FL technologies also tend to use methods such as model personalization, and weighing the updates based on the quality of local data, to maintain the integrity of the overall model.
Que: What retrofit solutions exist for integrating predictive maintenance into legacy industrial equipment lacking modern connectivity?
Ans: Here are the most common retrofit options you’ll find:
1. External Sensors: One of the simplest, most effective places to start is with non-invasive sensors - vibration, temperature, acoustic or current sensors - that can be attached to or near the equipment to monitor important indicators of wear, imbalance, or overheating, in most cases without needing to contact the internal components of the machine.
2. Edge Gateways: The sensors attach to or are connected to an edge gateway or small industrial computer that collects, processes, and analyses the information, on location. In fact, edge devices might even run lightweight artificial intelligence (AI) models to discover the anomalous situations and report alerts or summaries to the cloud and only when necessary.
3. Industrial IoT Adapters: If the equipment has basic control panels or analog outputs, you can use adapters, like OPC-UA converters or Modbus-to-IoT bridges, to digitize that data and send it out. This lets you monitor trends without changing how the machine works at its core.
4. Battery-Powered Monitoring Kits: In locations where running electrical wiring is difficult or not possible, wireless sensor kits powered by batteries offer a convenient and flexible solution. They’re easy to set up and use LPWAN or Bluetooth to communicate, often lasting several years on a single battery.
5. Cloud-Connected Platforms: Once the data is gathered, cloud platforms, or hybrid edge-cloud setups, can help visualize equipment health, predict possible failures, and plan maintenance ahead of time. That way, you reduce downtime and extend the life of your assets.
Que: What security mechanisms are essential to ensure the safe deployment of autonomous AI agents across critical industrial infrastructure?
Ans: Deploying autonomous AI agents in important industrial environments, such as power plants, manufacturing lines, or chemical plants brings major benefits, but it also introduces serious safety challenges. To protect things, you need a solid, multi-level security strategy. Here are the main elements that create that approach:
1. Identification and Access Management (IAM): Like any user or system, AI agents need to be certified and authorized. Using devices such as roll-based access control (RBAC), certificate-based certification and zero-trust architecture ensures that only verified agents can perform important actions.
2. Data Encryption: It is important that all data flowing between AI agents, edge devices and cloud platforms are encrypted - both are sent (using TLS) and when it is stored. This helps prevent data from intercept or tampering.
3. Secure boot and firmware Protection: The equipment running these AI agents should use safe boot procedures and signed firmware to ensure that there is no malicious code during the startup.
4. Behaviour Monitoring and Anomaly Detection: AI agents should be continuously monitored for any unusual or unexpected behaviour. This includes detecting abnormal command patterns, irregular data access, or other deviations that may indicate a fault or security compromise.
5. Model Integrity and Validation: Before going live, the AI model needs to be completely tested and digitally signed. Once they are deployed, regular runtime checks should ensure that nothing is changed or tampered with.
6. Network Segmentation: It is best to separate important systems from the outer network and a less safe environment. AI agents should be allowed only to communicate within a tightly controlled area to reduce the risk of spreading a violation.
7. Audit Logging and Traceability: AI agent should be logged in with timestamps in every action. It is a super assistant to compliance, troublesome, and investigate any issue.
8. Fail-safe and Override Capabilities: Finally, AI agents must have a safe decline option. Human operators should always step and close or close when there is any concern about safety or accuracy.
To explore how AI at the Edge can empower predictive maintenance and drive reliability in industrial operations, let’s connect and discuss how MosChip can support your smart factory transformation.
About the author:
Nayan is currently positioned as Manager of the PES division at Moschip Technologies Limited. He has a total of 9 years of experience. He has extensively worked in Data engineering and IoT domains. He is responsible for technical architectures and cloud liftings.