Few things have propelled the Internet of Things’ dizzying growth in recent years as much as machine learning and the innovators who are pushing it. Independent, intelligent machines that can comb through data to make their own decisions are, to some, the only reason such a phenomenon as the IoT can exist in the first place. When it comes to IoT, the connected devices are expected to work with little human intervention.
However, no matter how intelligent machines become, human beings still need a way to monitor them, to check that everything is working as planned. Adding machine learning to IoT monitoring tools helps detect problems and anomalies and enhance the analysis for human operators. Monitoring and management systems can not only check the performance, but can also provide real-time visualizations of device activity, irrespective of their locations: robots on factory floors, sensors in shipping fleets or medical equipment in a hospital. IoT needs a system to identify unusual situations and alert when attention is needed, before equipment failure disrupts operations.
Industrial IoT Will Transform Many Industries
While much of hype around IoT focuses on consumer applications, like smart homes, connected cars and consumer wearables like wristband activity trackers, it is the IoT’s industrial applications which may ultimately dwarf the consumer side in potential business and socioeconomic impacts. The Industrial IoT stands to transform many industries, including manufacturing, oil and gas, agriculture, mining, transportation and healthcare. Collectively, these account for nearly two-thirds of the world economy.
IoT Interoperability is critical for maximizing the value of the Internet of Things. According to a McKinsey report “On average, 40 percent of the total value that can be unlocked requires different IoT systems to work together.” With its open APIs, Samsung ARTIK Cloud breaks down data siloes between devices and enables a new class of IoT applications and services. By connecting directly to ARTIK Cloud, Anodot provides a layer of analytics and real-time detection of incidents to the collected data.
AI-powered Analytics Automatically Monitors and Turns IoT data into Insights
Anodot analyzes the millions of data points that stream into ARTIK Cloud from various IoT sensors in homes, factories or other IoT implementations. Anodot is disrupting the traditional business intelligence industry with its AI-powered Analytics solution.
Anodot’s proprietary machine learning algorithms learn the normal pattern of behavior from the real time event data streaming into ARTIK Cloud, and detects anomalies from the spikes and dips in IOT real time data, sending alerts about any metrics that deserve greater attention. Anodot scores each anomaly based on how far “off” it is from the normal, correlating multiple related anomalies to avoid alert storms and aid in determining the root cause of any issue encountered.
Disrupting the static nature of Business Intelligence (BI) tools
BI tools are generally designed to help highly analytical individuals make very specific decisions, they are backward-looking, and they lack the ability to provide actionable information to front-line analytics teams. Anodot is disrupting the static nature of the Business Intelligence (BI) market, differing in several key areas:
- Traditional analytics and BI solutions deal with historical data, not this minute, not showing a real-time status. Due to these limitations, they typically look at only a subset of all the available data, yielding at best delayed and at worst incomplete results.
- Traditional BI tools that monitor data cope with just part of a problem, only focusing on the data they think they might need, while specific signals could get overlooked.
- Traditional BI tools struggle to get an integrated view of all business metrics, focusing on just a few key metrics
Anodot analyzes streaming data in real time and predicts the future behavior of each metric. Anodot automatically identifies what is happening and can ingest all metrics, focusing on just the important ones.
Anodot applies algorithms to large volumes of data in a more efficient manner to discover patterns or trends in the data — a task that BI tools were not designed to accomplish.
Predictive Maintenance Prevents Breakdowns
The Internet of Things can create value through improved maintenance. With sensors and connectivity, it is possible to monitor production equipment in real time, which enables new approaches to maintenance that can be far more cost-effective, improving both capacity utilization and factory productivity by avoiding breakdowns. Predictive maintenance and remote asset management, can reduce equipment failures or unexpected downtime based on current operational data.
Vastly improved operational efficiency (e.g., improved uptime, asset utilization) through predictive maintenance and remote management improve operational efficiency through predictive maintenance, and achieving results such as savings on scheduled repairs (12%), reduced maintenance costs (nearly 30%) and fewer breakdowns (almost 70%).
In the screenshot below, Anodot monitors factory data generated by IoT sensors. All machine parameters are tracked and learned in real time, correlating metrics temperature, vibrations and noise. Anodot identifies several anomalies, possibly indicating a problem.
Outlier Detection Makes Proactive When Maintenance Possible
Anodot can also be used to compare the performance of similar things, and detect outliers. As the sensors and components become more prevalent in industrial environments, it is possible to collect data from multiple industrial IoT components and correlate a particular component behavior with similar components. Anodot can pinpoint outliers not just within the data for one machine, but for multiple machines (input changes like increased temperature). This will significantly improve predicting the likelihood of equipment failure or a need for unscheduled maintenance to maintain equipment efficiency.
Remove Seasonal Fluctuations and Expose Real Trend In Underlying Data
Machine data has a seasonal component, cyclic patterns in observed data over time. For example lights that are turned on in the evening, turned off at night, and then on again in the early morning. On the weekends the behavior may be different. These and other patterns make it difficult for a human user to identify static thresholds to set for manual alerts, and in fact make most static thresholds irrelevant. Anodot, however, automatically learns how the data behaves, including all of its patterns and seemingly random behavior, correlating between external events (like holidays or weather changes) and the collected data metrics.
Making Sense of Your ARTIK Cloud Data
Diverse devices and “things” continuously send and receive data to ARTIK Cloud. Anodot helps make sense of what is happening all in real time. Without having to set any thresholds or even understand how the data is supposed to behave, Anodot can provide automated, pre-emptive alerts.