AWS IoT SiteWise: 2020 in review
In this post, we summarize the new features and enhancements AWS IoT SiteWise launched in 2020 to improve customer experience to optimize industrial operations, and improve productivity and availability of industrial equipment.
This post is summarized into the following sections:
- Enhanced security
- More options for data ingestion and edge processing
- Greater asset modeling flexibility
- Out of the box alarms on equipment data
- Enhanced data visualization for better insights
- AWS IoT SiteWise plugin for Grafana
- AWS CloudFormation support
- New region availability
- Learning resources
- What’s next
Security is the first priority at AWS and a shared responsibility between AWS and our customers. With many industrial customers moving to AWS Cloud for their Industry 4.0 digital transformation journey, security remains at the top of our minds and this past year was no different for AWS IoT SiteWise. With AWS IoT SiteWise, all of your data is encrypted by default using a service-managed encryption key. You can now encrypt this data using your own encryption key that is configured in AWS Key Management Service (KMS) as shown below in the AWS Management Console. To learn more, refer to Encryption at rest and Key management.
Additionally, AWS IoT SiteWise now supports Amazon Virtual Private Cloud (VPC) endpoints via AWS PrivateLink for the data plane application programming interfaces (APIs) – PUT and GET APIs. This enables you to securely send and receive data from within your VPC, without crossing the public Internet and without using public IP addresses. You can do so by creating a VPC endpoint as shown below for the AWS IoT SiteWise service to establish a private connection between your VPC and AWS IoT SiteWise. To learn more, refer to AWS IoT SiteWise and interface VPC endpoints.
More options for data ingestion and edge processing
For most industrial customers, in the event of network latency and/or intermittent connectivity, in addition to sending the data to the cloud, it is a must have to continue collecting and processing equipment data locally (on-premises) to keep the production lines running without disruption. In addition, customers want to explore optimizing network bandwidth usage by sending only processed data to the cloud, for example, applying deadbands to only send significant changes in measurements or applying filters to only send measurements that exceed a threshold.
At re:Invent 2020, we launched SiteWise Edge (Preview), a new feature of AWS IoT SiteWise that brings the same capabilities of AWS IoT SiteWise in the cloud to the customer’s premises. The SiteWise Edge software runs on AWS IoT Greengrass that is installed on local hardware such as third-party industrial gateways and computers, AWS Outposts, or AWS Snow Family compute devices. With SiteWise Edge, you can now organize and process your equipment data locally in the AWS IoT SiteWise gateway using the same asset models defined in the cloud. You can then read the equipment data from the local gateway using the same APIs that you use with AWS IoT SiteWise in the cloud. For example, you can compute and monitor metrics such as overall equipment effectiveness (OEE) locally for use in production line monitoring on the factory floor using SiteWise Monitor dashboards served locally from the AWS IoT SiteWise gateway. By processing data on-premises, you can reduce latency for local applications and ensure business continuity even if Internet connectivity is disrupted. For example, a local alarm application that relies on the data collected by SiteWise Edge can continue to detect and respond to changes in the equipment status. With SiteWise Edge, in addition to sending equipment data to a centralized industrial data lake in the cloud for cross-site operational view and advanced analytics (such as machine learning), you can choose to send changes or processed data to the cloud for selective measurements to optimize the network bandwidth usage. To learn more, refer to Process data locally with AWS IoT SiteWise.
In an industrial site, connectivity between an on-premises edge gateway and the cloud can experience intermittent interruptions due to either planned or unplanned events. As a result, we added a store and forward capability in our edge gateway to avoid data loss. You can now ingest late data that is up to 7 days old (extended from 15 minutes) to AWS IoT SiteWise in the cloud when recovering from a connectivity issue. AWS IoT SiteWise recalculates the related transforms and metrics automatically on arrival of the late data. To get the most out of this feature, upgrade the AWS IoT SiteWise connector to version 8 or later. Refer to late data ingestion to learn more.
In addition to the existing OPC-UA and MQTT protocol support, AWS IoT SiteWise now supports data ingestion from two additional industrial protocols (Modbus TCP and EtherNet/IP) that are commonly used by our customers connecting industrial equipment such as electrical power monitoring systems, meters, generators, uninterruptible power supply (UPS), and transfer switches. Before adding a Modbus TCP or EtherNet/IP data source, remember to deploy the Modbus-TCP Protocol Adapter connector or Ethernet IP Protocol Adapter connector on your edge gateway. You can add a Modbus TCP or EtherNet/IP data source to your AWS IoT SiteWise Gateway as shown below in the AWS Management Console. Refer to Configure a Modbus TCP source and Configure an EtherNet/IP source to review all the supported configurations.
For the OPC-UA data sources, AWS IoT SiteWise added the following two capabilities to further optimize the network bandwidth usage for sending data to the cloud:
- Deadbanding: You can now define deadbands for the OPC-UA source property groups (a property group is a set of measurements) to filter out and discard certain data at the edge before transmitting to the cloud. A deadband specifies a band of tolerable fluctuations in the incoming data values from your OPC-UA source. This is to minimize the number of data points to be sent and stored in the cloud, optimize available network bandwidth, and reduce cost. To choose how much data is sent to the cloud and how much is discarded, you can specify two types of deadbands (Percentage and Absolute) for the OPC-UA source property group. To learn more on how the two deadband types work, refer to Filter OPC-UA data ingestion with deadband ranges.
- Configurable scan mode: You can now configure OPC-UA scan mode to control the way you collect data from the OPC-UA server by choosing either Subscription or Polling mode. In subscription mode, the OPC-UA server transmits data only when it changes. This mode is helpful in ingesting data from sources that have a high rate of change. In polling mode, the connector polls the OPC-UA server at a specified frequency to read data points even if the values do not change. This is helpful in ensuring a constant rate of ingestion from sources that may have a low rate of change. To learn more on how this works, refer to Control data collection frequency with Scan mode.
Additionally, you can now select a custom AWS IoT Greengrass stream manager stream as the destination for the data coming from all three supported industrial protocols (OPC-UA, Modbus TCP, and EtherNet/IP). By default, all data would go to the AWS IoT SiteWise cloud service. By routing data to a custom stream, you can now author long-running AWS Lambda functions that can perform local data transformations or filtering, at the edge. For example, you can connect to a local on-premises ERP system to enrich the telemetry data streams before sending to the cloud. You can also setup an export for AWS IoT Greengrass on the custom stream to send data to AWS IoT Analytics, Amazon Kinesis Data Streams, and Amazon S3. To use this feature, upgrade the AWS IoT SiteWise connector to version 9 or later. For more details, refer to Choosing a destination for your source server data.
Greater asset modeling flexibility
With formula expressions in AWS IoT SiteWise, you can define the mathematical functions to transform and aggregate your raw industrial data to gain insights about your operations. AWS IoT SiteWise has expanded its formula expression capability of metrics and transforms to include conditional logic and string manipulations, allowing users greater flexibility in defining real time monitoring of IoT assets.
With Conditional functions support, you can now define conditional logic to detect malfunction of your expensive equipment, or flag deviations from typical equipment behavior. For example, to take an action when a sensor has breached a temperature threshold of 300 degrees Fahrenheit, you can simply define a transform in AWS IoT SiteWise with the conditional expression
if(gt(temperature, 300), temperature, none) which returns the “temperature” value when temperature is greater than or equal to “300”, otherwise returns “none” (no value).
With String functions support, you can now define conditional computations using string functions (concatenation, comparisons, case conversion, slicing, string length, and search) on user-defined transforms and metrics of incoming telemetry data and visualize the output on a SiteWise Monitor dashboard. For example, you want to convert the equipment status from strings to boolean values for downstream application consumption. To achieve this, you can simply define a transform in AWS IoT SiteWise with the conditional expression
if(equals(equipment_status,"Maintenance"),0,1) which returns “0” when the equipment status is “Maintenance”, otherwise returns “1”. For a list of string functions supported, refer to Using strings in formulas.
Also, AWS IoT SiteWise now supports the Boolean datatype for properties, pseudo constants such as NONE, and computations with standalone attributes. In AWS IoT SiteWise, Booleans convert to their number equivalents, i.e., “true” is converted to “1” and false is converted to “0”. For example, you have an equipment property named “Idle” that can have a value of “0” or “1” where “1” indicates the idle state and “0” indicates the running state of the equipment. You can calculate the total equipment idle time for a given time interval with this expression:
IdleTime = statetime(Idle). To learn more, refer to Using formula expressions.
Out of the box alarms on equipment data
For Industrial IoT applications, data monitoring alone is not enough. You may also want to get alerted on anomalies or malfunctioning of your costly equipment. With alerting, you can prevent downtime and reduce operational expenditure associated with downtime or capital expenditures associated with repairing or replacing the equipment.
To help with this, AWS IoT SiteWise launched Alarms, a feature (currently in preview) using AWS IoT Events under the cover in a seamless way, that allows you to create alarms on your equipment data. You can also setup, visualize, and manage rule-based alarms for devices, equipment, and processes in AWS IoT Events itself for any device data going directly to the service. You can configure these alarms so that you receive alerts via SMS or email in near-real time when equipment data breaches thresholds, allowing operations teams to take timely actions to reduce unplanned downtime.
To integrate the alarm notifications with your own ticketing systems, you can also configure actions to other AWS services such as AWS Lambda, Amazon Simple Queue Service (SQS), and Amazon Simple Notification Service (SNS). Once you create the alarm, you can use existing AWS IoT SiteWise APIs to query the current or historical alarm state over specific time intervals. With AWS IoT SiteWise Monitor, operations teams can visualize equipment alarms, analyze alarm data against live and historical data trends, determine corrective actions to take (like scheduling equipment repair), and manage the end-to-end alarm workflow (like acknowledging an alarm with comments about actions taken). To learn more, refer to Monitoring data with alarms.
In the Cookie Factory example shown below, an alarm rule is configured to trigger a severity 3 alarm (e.g., severity 1, 2 and 3 corresponding to high, medium and low severity) when Mixer 1 exceeds 200 rotations per minute, and send an SMS when the alarm is triggered for necessary corrective actions to take by an operations team. Once the alarm is defined, it can be dragged and dropped just like any other asset property in the SiteWise Monitor dashboard to easily visualize the status of the alarms. When the alarm for Mixer 1 is triggered, an SMS notification is received on the configured mobile device. In addition, operations team is interested to visualize the historical status for the Zone 2 equipment using status timelines chart, and how the abnormal mixer behavior and asset downtime impacting the overall OEE.
Enhanced data visualization for better insights
AWS IoT SiteWise Monitor, a fully managed out of the box web application of AWS IoT SiteWise, now supports Status charts, Table charts, and Scatter charts. With these new chart types, you can now choose the chart type that makes the most sense for your data you want to visualize in your AWS IoT SiteWise Monitor dashboards. For example, if you have a temperature indicator of your sensors that can be high, medium, or low, you could display each state in a different color using a status grid for current status or a status timeline for historical status.
Additionally, AWS IoT SiteWise Monitor now supports Trend lines, a linear regression line that best approximates the relationship between measured data and time. With trend lines, you can now intuitively identify gradual shifts and changes in your live and historical data simply by adding trend lines to any of your line, scatter, and bar charts. For example, a wind farm operator may observe a gradual decline in the power output trend line from a wind turbine while observing there is a flat and steady wind speed trend line. The operator may correlate this insight to a power conversion efficiency issue with the wind turbine and call on field technicians to inspect and troubleshoot it.
The following video shows how you can easily build dashboards and browse your data and metrics from AWS IoT SiteWise for a wind farm using AWS IoT SiteWise Monitor.
AWS IoT SiteWise plugin for Grafana
In 2020, AWS and Grafana Labs launched the AWS IoT SiteWise plugin for Grafana. Grafana is a data visualization platform that some of our customers already use to visualize and monitor time-series data in operational dashboards. Users of Grafana 7.3.0 and higher can use this plugin to visualize AWS IoT SiteWise asset data in Grafana dashboards. This also lets you easily combine data from multiple sources (for example AWS IoT SiteWise, Amazon S3, or Amazon CloudWatch) when needed and monitor them all using a single Grafana dashboard. You have two options to use for the plugin: Local Grafana servers that you manage or AWS Managed Service for Grafana. To learn more about the setup, refer to Integrating with Grafana.
The following video shows a sample Grafana dashboard which is monitoring key operational metrics for a wind farm data from AWS IoT SiteWise:
AWS CloudFormation support
AWS IoT SiteWise now supports AWS CloudFormation so you can create and manage AWS IoT SiteWise resources by authoring CloudFormation templates for Asset Models, Assets, Gateway resources, and AWS IoT SiteWise Monitor resources such as portals, projects, and dashboards. You can reuse these templates across accounts and regions to ensure consistency. To learn more, refer to AWS IoT SiteWise resource type reference in the CloudFormation user guide.
New region availability
In July 2020, AWS IoT SiteWise was made generally available in US East (N. Virginia), Europe (Frankfurt), US West (Oregon), and Europe (Ireland). AWS IoT SiteWise is now available in two additional AWS Regions – Asia Pacific (Sydney) and Asia Pacific (Singapore). We continue to expand to new regions, for the most current list of regions where AWS IoT SiteWise is available, visit the AWS Region table.
AWS IoT SiteWise is a fully managed AWS service that provides an end-to-end solution to collect, organize, analyze, and monitor data from industrial equipment at scale to help you make better, data-driven decisions. With AWS IoT SiteWise, you can monitor operations across facilities with centralized data, continuously compute common industrial performance metrics, and create applications that analyze industrial equipment data to optimize operations, prevent costly equipment issues, and reduce gaps in production. To get started, refer to Getting started with AWS IoT SiteWise.
To learn more, refer to the following:
- How Genie® (a Terex® brand) improved paint quality using AWS IoT SiteWise – In this post, we discuss how Genie used an AWS IoT SiteWise based solution to ingest, organize, and analyze critical process parameters from the paint system. This solution identified inconsistent and improper pretreatment parameters in near real-time and enabled Genie to apply necessary corrective actions to improve the downstream paint quality of Genie lifts.
- Collecting, organizing, monitoring, and analyzing industrial data at scale using AWS IoT SiteWise – In this post, you will walk through a solution using AWS IoT SiteWise.
We are looking forward to 2021 to enable you with data-driven insights to optimize your industrial operations, and improve productivity and availability of your industrial equipment. Stay tuned to the What’s New with AWS channel for AWS IoT SiteWise to learn further updates. Contact us if you want to implement AWS IoT SiteWise in your environment and need assistance.