SMIC is a software service which enables data interpretation for integrity management of floating facilities. SMIC's major components are:

- Real-time failure detection algorithm running on the cloud or local servers
- Web-based user interface providing advanced analytics and full data transparency
- 24/7 automated alerts via email, SMS or web-hooks
- Regular performance reports on the response of the mooring system
- An interface for all input data streams (either in software or hardware)

Refer to the system architecture page for more information on each of these aspects.

SMIC Advantages

1. Easy to retrofit to systems using existing instrumentation (e.g. vessel GPS position, environmental monitoring)
2. Does not require instrumentation in the load path which is costlier to install and maintain and less reliable
3. Automated alerts and reports
4. Interactive analytics interface

The SMIC web interface allows users to view the status of the system and navigate multiple tabs displaying vessel data, logs of alerts and system settings. The SMIC web interface can be customised for your company, with the addition of a logo and company asset names. Authorised stakeholders can then access SMIC monitoring for multiple company asserts via single secure web access.

The most important features are detailed below. 

Offset Monitoring

SMIC enables visualisation of offsets in cartesian coordinates with associated probability distributions. Both recent and historical data are presented within the same visualisation to enable comparison.

Scatter Plot

- Interrogate data by hovering with the mouse on the point of interest to identify offset values and time
- Toggle Watch Circles on and off
- Toggle historical data on and off
- Zoom in on an area for greater detail

 Occurrence Distributions

- Both axes have associated probability density function (PDF) plots of the offsets
- PDFs are estimated with Kernel Density Estimation methods
- Compare recent and historical distributions for instant identification of asset drift  

Data Range

- Select the reference date/time through a pop-up calendar
- Control data range for recent and historic data through slider bars
- Select multiple historical months for comparison through drop-down menus

Historical Analysis

SMIC enables quick analysis of trends in offset data and associated mooring status through visualisation of the Eastings and Northings time histories. The time range and zoom level are under control of the user, allowing them to assess both long- and short-term trends.

 Offset Time History Plots

- Broad time ranges, from 1 day to 1 year, display the hourly average offset data with ±2 standard deviation envelope
- Short time ranges, under 24 hours, display raw data within hourly mean ±2 standard deviation envelope
- Interrogate the data by hovering the mouse to determine the offset for any target time

Background Colour Mooring Status indicates the SMIC mooring status at the associated historical time

Data Range

- Pan the time period by sliding the bottom bar
- Adjust the period length from a few minutes up to a year by dragging the sides of the bar

The SMIC system also integrates behind-the-scenes automated alerting and reporting functionality to keep stakeholders up-to-date with the health of the system without having to login to the web interface.

Alerts

SMIC's alert functionalities serve to notify stakeholders of the most important outcomes of SMIC's analytics. Most significantly, this means identification of potential mooring failures are communicated as soon as possible, without the need for around-the-clock offset monitoring. The full history of past alerts is also displayed on the web interface.

Alert Types

- Mooring integrity status alerts
- Watch Circle excursions
- Detection of poor data quality
- Re-establishment of good data quality

Alert Delivery Methods

All of the above alert types can be delivered in the following forms:

1. Email
2. SMS
3. Web hooks or APIs

Alert recipients are completely flexible, meaning each stakeholder can be signed-up to receive any specific alert in any delivery format.
 

Reporting

SMIC offers both Dashboard Snap Reporting and Automated Periodic Reports. SMIC reports collate large pools of data into digestible summaries of mooring health in portable formats.

Dashboard Snap Reporting

On the web interface, the user can curate the data for specific periods and assets on the dashboard and then print the dashboard view neatly to pdf via a dedicated button.

Automated Periodic Reports

SMIC issues automated PDF or HTML reports on a weekly, monthly and yearly basis, providing summary statistics for the reporting period and comparisons to historical trends. These reports are sent via email to designated stakeholders.

The reports typically include:

- Current Mooring Status
- Summary vessel offset statistics
- Summary of data quality in reference period
- Watch Circle exceedances
- Summary of notifications/alarms
- Visual representation of data (scatter plots, time histories, occurrence distributions, etc.)

The automated periodic reports are fully customisable to meet the requirements of the client/asset.

SMIC performs data analytics on mooring offset behaviour and produces alarms in the event of a potential mooring failure or Watch Circle limit exceedance. SMIC data analytics have been developed based on AMOG's vast experience in understanding the dynamic behaviour of floating offshore facilities and their associated mooring systems.

Using DGPS offset data alone, SMIC's failure detection algorithms learns the range of healthy responses expected by the mooring system and warns of anomalous mooring behaviour. This alerts managers in charge of mooring integrity and system safety to investigate potential damage early after a single or multiple mooring line failure.

With the provision of an environmental data stream, the data analytics can be extended to include full machine learning prediction of mooring line failures as well as forecasting facility behaviour during cyclones or large weather events.

SMIC data analytics can also review and assess the quality of input data streams, notifying stakeholders of disruptions to and re-establishment of data streams.

Inputs

At a minimum, DGPS data alone suffices for SMIC analytics to successfully detect mooring anomalies. For greater accuracy, SMIC can incorporate the following into its models:

- DGPS offset data
- Mooring line monitoring data (load cells, inclinometers)
- Loadmaster data
- Environmental monitoring data (wind, wave, current)
- Environmental forecasting data

Model training data can either be supplied by the client or can be generated by AMOG in-house, calling upon AMOG's years of expertise in hydrodynamic modelling.

The input data stream can be received in a number of formats, including secure FTP or via an API.

Data Analytics Process

Incoming data undergoes the following process after arrival on AMOG's SMIC servers:

1. Data quality checks & notifications
2. Watch circle excursion checks & notifications
3. Mooring integrity analysis, traffic light system updates & failure notifications
4. Dashboard updates & failure visualisation
5. Periodic mooring health reporting

One of the key characteristics of SMIC is its ability to use already installed monitoring equipment on a floating platforms or vessels. For this reason the system is cost-effective and easily retrofittable without interruption to normal operations, especially in comparison to monitoring technologies such as load cells or fairlead angle measurement devices.

SMIC uses key environmental and vessel data streams to train several bespoke machine learning algorithms on the performance of the mooring system. These are typically vessel offsets and wind-wave-current conditions. This training generates a model of the system. The model is then used to predict the response of the asset to current in-field environmental conditions. The prediction is compared to the measured system performance over a sample time period. If the prediction diverges from the measured performance, an alert is automatically generated and sent to nominated stakeholders, allowing investigation of the anomalous response.

SMIC's algorithms are trained based on the actual installed system, rather than hydrodynamic models generated during engineering design.
This approach better captures the actual behaviour of the system in the field, which a hydrodynamic model may not accurately capture, owing to discrepancies between the design and as-installed/as-operated system (e.g. installation tolerances, system degradation over time, or simply conditions never considered in the design phase).

SMIC incorporates a resilient combination of bespoke machine learning algorithms, capable of coping with irregular, low quality or missing data feeds. This resilience improves over the life of the system, with more data improving the model of the underlying system. AMOG's machine learning algorithms have been developed and trained on actual floating units using data-streams from systems already aboard. This system training included identification of a real-life line failure event.

Please contact AMOG on +61 3 9542 3700 for more information, or you can click to contact the SMIC Engineering Team.

Click below for more articles, case studies, announcements, and information about SMIC.

Articles

Why should you use a smart system to monitor mooring integrity?

Why SMIC is the smarter way to do mooring monitoring and integrity assessment

From Model to reality: practical insights from mooring integrity monitoring using vessel position data

Technology Listings

Find SMIC on Australia's Energy Technology Platform, hosted by NERA (National Energy Resources Australia)

Press

Find out more about SMIC's deployment on the Ichthys gas field floating facilities for INPEX Australia

You can contact the SMIC Engineering Team at AMOG's Melbourne office on +61 3 9542 3700 or speak to one of our offshore engineers in the USA on +1 713 255 0020.