Data will be the primary driver of the AI-based revolution making machines on the factory floor smarter. Machine data—generated by the machines themselves about their performance and collected by sensors about their environment—will be crucial for any effort to optimize outputs.
Real-time data allows factories to detect when repairs are needed on the assembly line or when changing environmental conditions around the machines need mitigation. With sufficient data, factories can identify conditions that signify an impending machine malfunction and perform preventive maintenance to avert it.
This proactive approach will reduce downtime by allowing factories to address machine issues before a breakdown occurs, improving production efficiencies and reducing costs by ensuring that machines can perform at their peak.
The challenges of diverse hardware in factories
Most factories operate with a diverse array of hardware. They use the best tools for each job, which often means integrating machines from different manufacturers. However, this diversity leads to incompatible devices and data silos, preventing the realization of true smart factory efficiency.
Incompatibility among machines can manifest in various ways. Sometimes, machine operating systems struggle to communicate with each other and require specialized knowledge like programming or querying languages to tap the data from each machine. In other instances, machines produce data in different formats requiring additional processing before analysis. Discrepancies in data granularity, definitions, integrity, and security can also lead to misalignment and erroneous insights, undermining the original purpose of collecting and analyzing data.
Overcoming these hurdles enables you to use machine learning to automate insightful decision-making and improve efficiency across a fleet of machines. Data from diverse machines becomes more usable for actionable insights when those machines are interoperable and their data is accessible and easily shared.
Leveraging the cloud to expand machine data sharing
It’s worth taking a moment to talk about taking advantage of the cloud at a factory, which is often perceived as a risk. But it doesn’t have to be. In fact, by normalizing and preparing data for training ML models that make your machines more efficient in the cloud, you eliminate the need to carry out that activity closer to the physical operations on the factory floor, or not doing it at all. A unique approach to machine interoperability is essential to bust through the obstacles that prevent disparate machines from sharing data with each other and the cloud in a way that encourages efficiency without introducing new risks.
Viam’s platform, supported by its Core architecture, can be integrated with all other hardware that is connected to a computer that runs the Viam server binary, which can be installed on any machine. Viam server manages communication between hardware drivers and provides access over local or internal networks, enabling software services like data management or machine learning. Viam also utilizes protocols, frameworks, and a secure and efficient API layer similar to peer-to-peer networks, enabling devices to communicate with the cloud and each other with ease.
The platform has a wide range of pre-built drivers for major operational technology machines typically used in factories, such as PLCs, SCADA systems, and DLS 3D printers, so that Viam can easily and securely plug those machines into the cloud. Developers can use Viam’s Modular Registry, a marketplace of open source modules for machine functionality, to easily create custom drivers for less common hardware, deployments, and software updates.
To ensure data protection, Viam includes rigorous security protocols. It is SOC2 Type I and HIPAA compliant, featuring strict access controls, privacy measures, and data encryption for all communication between smart machines, mobile applications, and the cloud.
Data management designed for interoperability
Transforming data into actionable outcomes is challenging, especially without a data management platform designed for physical devices that can integrate with popular analytics tools. Typically, manufacturers must synthesize various tools from multiple vendors to manage all their machine data from a single view. This patchwork solution involves gathering machine data, uploading it to the cloud, storing it, displaying it, and processing it to train ML models and run analytics.
In contrast, Viam provides out-of-the-box cloud pipelines designed to easily integrate with machines, facilitating their monitoring and management. These cloud and data synchronization options surpass the features of typical cloud storage providers.
Essentially, Viam abstracts cloud storage into a holistic platform that collapses a wide range of cloud storage management functions into one solution and allows everything to be accessible through a wide range of APIs and tools. This allows factories to streamline many different tasks through one platform and screen that eliminates the constant toggling more generalized cloud storage platforms usually require.
If critical data flows on machines across a network are disrupted, the data is usually lost, leading to gaps in reporting and modeling. However, Viam’s platform accounts for such intermittent connectivity, storing data and configurations locally and then synchronizing them whenever convenient. This allows factories to collect the necessary data to analyze performance and functionality to help improve efficiency and productivity across all machines, even at the edge or in other situations where connectivity may be irregular.
Factories don’t need to capture all data, as trying to do so can quickly inflate cloud storage and analysis costs. Sifting through excessive data to find specific insights is tedious and often unnecessary. Thankfully, with Viam’s selective data capturing, factories can configure machines to only upload specific sets of data to the cloud, making it easier and more practical to store the information they actually need.
Viam also enables gathering and labeling machine data in the cloud, training machine learning models, and deploying those models to an entire fleet of machines in a streamlined way, providing machines with intelligent guidance that can make them smarter more quickly.
The good news is that your factory is probably already generating the data you need to make your factory more agile and sustainable. Viam helps you capture that data from your diverse hardware and turn it into actionable insights that prevent downtime, enhance efficiency, and ultimately increase yields.
Getting started with Viam data interoperability
Viam is free for developers to start using. Here are a few steps to begin exploring the data management Viam provides:
- Sign up for a free Viam account
- Follow the steps in our documentation to install Viam server on any machine
- Once Viam server is installed and you have an account, start configuring data capture from the machine
- View the captured data within Viam or configure it to visualize in your preferred tool.
For a formal demo of Viam’s machine data management, you can get in touch with our team
