Mtell Previse the state-of-the-art toolset for machine learning-based analysis of related time-indexed data for preparing Mtells pattern recognition monitoring agents. Also supports general purpose learning of complex behavioral data patterns for many optimization and deci-sion-making initiatives in any organization.

Mtell View Mtell View fuels situational awareness for personnel remotely over-seeing equipment at diverse locations. Early alerts combined with aggregation and correlation effectively steer investiga tions

and root cause analysis by equipment type, usage, and across sites.

Mtell Reservoir a high performance repos-itory for time-series data, maintenance and operational events, and other relationship data.

Mtell CloudSync and other third party data ingestion tools provide controlled, easy connectivity and input from disparate sources.

Third Party Extensions through open APIs Mtell Summit permits other applications to enter data into the repository. APIs also al-low the extraction and processing of data in contemporary client applications such as R, Mathematica, Matlab, Apache Spark, etc.

Mtell Summit is the premier remote monitoring center application for gathering and combining time-series and descriptive, relationship data for complete analysis and benchmarking.

Mtell Summit comprises a suite of foundation Mtell applications including Mtell Reservoir, Mtell CloudSync, Mtell Previse, and Mtell View. Summit allows open access to third party applications, including its open repository, to combine time synchronized data from any source. While Mtell tools provide analysis and learning, open APIs allow delivery of any raw data and computational results into diverse client applications for display, reporting, or extended analysis.

Mtell Summit enables BIG data scalable to thousands of sites, millions of assets, with billions of sensors, and trillions of sensor readings. Multiple hardware nodes assure outstanding disk input/output and CPU processing capability. Federated views of equipment across multiple sites deliver enhanced asset health monitoring, and facilitate remote maintenance workprocess across many locations. The diagnostic capability, accuracy, and the range of condition monitoring using simultaneous machine learning across many machines at many locations are all increased using Mtell Summit. Extensive analysis and data delivery into other applications extends the use cases for Mtell Summit.

Storage for all sensor time-series data

Federated views across multiple manufacturing sites

Local and remote data center synchronization

Power to process large datasets

Foundation for predictive analytics: Mtell Analytics plus thirdparty reporting and analysis tools including R,Mathematica, Matlab, Apache Spark, etc.

Scalability to multi-CPU clusters for:- Increased data processing requirements - Faster disk I/O operations

Scalability Plus Performance

Tossing the old historian over the IT fence and running it on a bigger computer or cluster will not meet the requirements

of the users and applications at the enterprise. Instead, a new design, Mtell Summit fully meets the demands.

The plant floor model of Mtell Previse is extended into

Mtell Summit for extra duties on much larger (federated) data sets from multiple sites. Summit also supports additional analysis techniques for other optimization and decision-making services that can extend across diverse manufacturing processes equipment at many locations.

Transfer Learning is a key capability, where Mtell Summit learns on one machine and transfers that learning in the form of pattern signatures to monitoring Agents on similar machines at other locations.

Mtell Summit unlocks a further advance in retaining and sharing knowledge across fleets

or pools of equipment. Mtell calls this process Population-based Learning where Mtell Summit combines group analysis and learning of behavioral patterns from similar processes and equipment, regard-less of where they are located. Summit aggregates all the sensor information for groupings of similar equipment to massive sets in Mtell Reservoir. Internally deep learning extracts the patterns of operations and failures, learning the shared behavioral characteristics of the entire set at the same time. Mtell Agents produced this way provide

a new level of accuracy of pattern recognition, with only limited labeled data requirements. Such Agents are readily shared across the set members even if they are located at different sites and with different customers.

Starting from day one, newly installed equipment of the same type, sensors, and usage can be

equipped with Agents for monitoring normal and failure behavior that were prepared from older working equipment.

By gathering all that time-series data into a great big storage, many other things are possible and desirable. A BIG data reservoir serves as the storage and source of all

related data that is connected by time-stamps. Additional data such as notes, work

orders, photographs, videos, etc., can be inserted into the archives to be readily accessible whenever a user calls up a relevant historical trend. The Mtell Reservoir BIG Data sets allow analysts to perform ad hoc discovery, organi-zation, and enrichment to prepare data for other analytical tools, reports, and dashboards.

Remotely connecting operations and maintenance systems facilitates the highest performing assets at the lowest risk, and best financial performance.

Mtell Previse

CloudSyncsophisticated transfer

Mtell Reservoirlarge volume data

complex processing

Sitesincluding fleetsof equipment

Mtell View delivers contextually developed data about the performance and failure characteristics of assets and process equipment. At the enterprise level, Mtell View provides an intuitive navigation scheme that quickly alerts users, guiding them rapidly and effectively to important and prioritized information. Federated

views allow subject matter experts (SMEs) in a remote monitoring center to oversee equipment at diverse locations simultaneously. All information about any assets including condition-based alerts, maintenance work orders, and Agent properties, is aggregated and correlated in views highlighting situational awareness. Heat maps give extremely visual ways to show concentrations of specific degradation and failure in many dimensions. Analysts can quickly perform investiga-tions and root cause analysis by location, across sites, across asset groups/fleets, by failure mode,

equipment type, customer, usage. Consequently, the failure profiles and associated risk are immediately

evident. Such clarifying views are essential to owner-operators, remote service providers, and original equipment manufacturers who wish to monitor and manage distributed assets from a central location.

At the enterprise datacenter, a new caliber of repository for historical data retention and delivery must meet the needs of more users, diverse applications, and emerging BIG data applications. Mtell Reservoir replaces and extends contemporary time-series historians to leverage enormous advances in computer hardware and software. For example, Mtell Reservoir recorded total data ingestion rates at 100 million points per second on a modest 4 node Hadoop cluster and scales almost linearly with additional hardware.

Mtell Reservoir leverages the Hadoop and OpenTSDB (time-series database) software technology. The Apache Hadoop software library allows for load-sharing by distributing the processing of large data sets across clusters of computers. Hadoop scales from a single server to thousands, each offering local computation and input/

output storage. Additionally, the OpenTSBD is a data management framework designed specifically for handling

time-synchronized and indexed data. Implementing the Mtell Reservoir on Hadoop with OpenTSBD provides large

improvements over traditional plant historians, especially for retrieval and display of very large data sets. Additionally, Mtell Reservoir facilitates specific maintenance process

library functions, general purpose archiving, and information comparison functions, including report generation and third-party analysis.

Mtell View bundled visualization application

Mtell Reservoir

Mtell Reservoir is the full function enterprise storage for all time synchronized data.

Mtell CloudSync provides extremely high data ingestion rates. CloudSync connectivity streams data from plant historians, but also accepts specific batch uploads of comma-sep-arated value (CSV) files and custom

developed data input services.

Its elegant and sophisticated bi-direc-tional architecture ensures CloudSync performs stream-based processing across challenging and bandwidth limited network connections such as satellite links.

Transmitted streams include sensor data values, alerts, events, and maintenance activities. Automatic,

lossless data compression means more efficient data transfers, and dynamic

throttling keeps transfer within configured

bandwidth limits. Signal prioritization assures the most pertinent data are received first, and the system will

recover older data as bandwidth becomes available. CloudSync also delivers machine

learning signatures from Mtell Summit into monitoring Agents at remote sites.

Mtell Summit provides comprehensive asset health monitoring and analysis for myriad machines at multiple locations.

Facilitating the 3 Ps of Maintenance Analytics

1. Performance where information is readily availablein views, charts, and trends to examine current andpast asset performance.

2. Predictive where the solution can detect and warnof impending equipment failures well before seriousdamage occurs, including root cause analysis andprocess defect profiling.

3. Prescriptive offering key advice on mitigation,ordering inspection or repair, along with adviceon avoiding impending issues.

Mtell CloudSync

Asset Health Monitoring

Focus on the future, not the past. A powerhouse tool set assuresMtell Summit predicts what can happen and advises the action to avoid it.

Comprehensive remote maintenance monitoring has been elusive; promised by many but never really delivered. Most solutions provide simple graphics indicating present and past asset behavior, and require remote personnel to manually search in an attempt to discern problems. With such limited function-ality, remote centers offer little more than post incident phone

support. Mtell Summit changes all that with real, accurate predictive failure warnings generated at the manufacturing location, and/or at the Data Reservoir. Mtell provides precise, early warnings of impending issues, alerting WHEN and WHY a failure would happen, and well before damage occurs. Armed with accurate predictions, subject matter experts (SMEs) at a remote operating center can oversee equipment located at many sites. SMEs are forewarned, can investigate and take immediate action to alleviate the issue. Resulting recommendations to adjust production or arrange minor servicing could avoid the problem altogether, or defer maintenance to a convenient time.

The remote asset health worker can use the Mtell Reservoir for many horizontal analytical tasks that improve both manufacturing process and equipment efficiency, including:

Reporting and Analysis

Investigation of trends

Profiling failure signatures across pools ofsimilar equipment

Root cause analysis

Sub-component analysis

Process analysis that permits review of multiple sensordata streams across time

Equipment benchmarking; enabled by managingnameplate and brand information across equipmentto compare variances in performance affected by

location, usage, etc.

Batch/discrete process analysis, including comparingbehavioral patterns across multiple batches

Signature Analysis

Investigation comparison of signatures across timeand across similar equipment

Anomaly detection and conversion of anomaly failuresinto more precise signatures that detect far earlier thananomalies

Event signatures including investigation of hidden failures

Failure signatures the most precise and earliest wayto detect degradation

Efficiency signatures the inverse of degradation,inquiring why some equipment operates more efficiently

Process defect signatures similar to failure detectionon equipment, where the focus is on discrete eventssuch as a batch processing where variations can occuracross batch runs

Remote & Predictive Asset Health Monitoring

The content of the Mtell Reservoir is not limited to cross-site federated views and machine learning for asset health management. Time-series sensor data are lightly governed before ingestion; a cleansing procedure assures all data points are valid and within range before ingestion and machine learning. Consequently, the rich content is available for many business users to explore, combine with other data, build reports. Data can be extracted and processed by alternative client applications, or structured data warehouses, to answer questions that have not been possible or practical in the past.

The Mtell Reservoir is more than an elevated plant historian with a bump in CPU speed. First it is the scalable high perfor-mance industrial big data reservoir for time-series sensor data streams and event records. Second, it is a library containing a whole host of information about assets and asset performance; mappings to equipment models, sensor templates for fleets

of similar equipment, and a failure library based on the ISO 14224 standard.

Mtell Summit has been implemented successfully across several industrial sites. Reliability engineers can use Mtell Summit for maintenance workflow management, and can also generate and share unlimited machine learning agents at sites across

the globe. Mtell Summit is the essential solution that merges process and maintenance data from multiple locations and presents them in rich, intuitive graphical displays in web browsers, tablets, and smartphones.

Reporting and Analysis

Process performance

Equipment performance

Model types

Site performance benchmarks

Equipment benchmarks

Breakdown by location, operator, etc.

Industrial Machine Learning Library

Deep learning

Signature library

Sensor knowledge base

Sensor ranking/importance

Industrial Library

Industrial signal processing

Feature extraction

Hierarchical sensor model

Virtual sensing

Sensor upset detection

Shift change / MaintenancePerformance logs

Universal plant model

Equipment taxonomy Nameplate parameters Linked to sensors

A Time-synchronized Data Repository for any Use

An Information Library

CloudSync Remote Site Connect

Big Data Scalability

Equipment Model

Multi-Site Tag Namespace Analysis & Reporting

World Class Performance

Industrial Strength Processing

On-ramp to big data, sends data from remote facilities

Configurable bandwidth throttling

State-of-the-art compression and security

Supports live streams and batch-oriented backfill

Resilient synchronization with store-and-forward and system restarts

Leverages Hadoop and multiple editions including MapR, Cloudera, and Hortonworks

Scalability add nodes for extra CPU and I/O capacity

OpenTSDB for flexible management of time series data

For unique names from multiple remote plant historians

Equipment types, associations, and sensor groups

Drag-and-drop report generation

Key filtering and management for all reports based on equipment hierarchy (site, equipment, sensors) and sensor templates (models, sub-assemblies)

Correlates sensor streams with maintenance work orders, operator actions, and produc-tion activities/events

Mashup trends overlay events on sensor signals

Low-latency response times for analytical applications

Mixed workloads: quick trends to machine learning

Detects issues in sensor reliability & calibration issues

Intelligent interpolation using uni/multi-variate techniques

Incorporates virtual sensors, rules, and calculations

Extensible equipment model library & taxonomy

Failure Code Library based on ISO 14224

Automated import of equip-ment hierarchy and taxonomy from EAM/CMMS systems

Mapping of sensor values into equipment templates

Cutting Edge Features

Mtell Summit provides high performance, industrial strength, predictive analytics for enterprise-wide decision making.

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