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With the proliferation of vendor solutions in the Industrial Analytics marketplace, it may be tempting to bypass the Business, Functional and Technical Requirements phase of the purchase process. The danger of diverging from standardized requirements and due diligence practices is that you become over-dependent on the information provided by the solution vendors.

The selection process for an IIoT Industrial Analytics solution is based on industry, facility and solution-specific requirements. Although no two requirements documents are identical, there are several functional and non-functional requirements common for most analytics and maintenance scenarios.

Below are key variables to consider when defining the requirements for an Industrial Analytics solution for Predictive Maintenance. Use this checklist as a starting point to develop your Requirements Document. For each variable, we provided a Basic or Baseline Functionality and an Optimal or Best Practice Functionality.

Please note that our evaluations are high-level, and individual vendor solutions will vary. We strongly recommend developing your own scorecard based on the list of Functional and Non-Functional requirements identified by the internal stakeholders of your industrial facility.

Practitioners’ Guide: What can go wrong? Based on our experience at Presenso, the following are the most common mistakes made during defining requirements for Industrial Analytics.

  • Excluding key stakeholders in the process
  • Failing to agree on a prioritized list of requirements
  • Over-reliance on vendor pitches
  • Divergence between views of production facility and headquarters
  • Conflicting agendas of IT and OT
  • Limited understanding of underlying Machine Learning data science

High Priority Functional Requirements for an IIoT Predictive Maintenance Solution

The following Functional Requirements need to be defined by stakeholders within your organization:

  • Interoperability / Open Architecture
  • Asset and Sensor Neutrality
  • Alert Generation
  • Machine Learning Methodology
  • Asset Visualization

#1 Interoperability / Open Architecture:  There is no standard or uniform IIoT infrastructure platform. The key consideration is whether the analytics solution works with multiple platforms or is a closed add-on to one platform.

Baseline versus Best Practice Functionality

Baseline Functionality Closed analytics application that is built for one IIoT infrastructure platform.
Best Practice Functionality Analytics solution that interfaces with any of the major platforms including GE Predix, SAP Hana, etc.

#2 Asset and Sensor Neutrality: The key consideration is whether the solution functions in heterogeneous plant environments with data from all production assets. In some cases, the solution is tied to one class of sensors or processes.

 Baseline versus Best Practice Functionality

Baseline Functionality Closed analytics solution designed for a single sensor, vendor or asset class.
Best Practice Functionality Solution is agnostic to sensor type, vendor, asset age or class.

#3 Alert Generation:  When a machine degradation or potential asset failure is detected, this is communicated to the relevant facility stakeholders.

 Baseline versus Best Practice Functionality

Baseline Functionality Reports of sensor data abnormalities that need further internal analysis to be actionable.
Best Practice Functionality Real-time and actionable alert generated and communicated electronically to facility staff on a role-based basis.

#4 Machine Learning Methodology:  Each Predictive Asset Maintenance solution is based on a Big Data methodology. Is this a manual process or is Artificial Intelligence used to automatically select the optimal algorithm for the specific scenario?

 Baseline versus Best Practice Functionality

Baseline Functionality Manual statistical modelling by data scientists using off-line sample data that is then extrapolated to a machine asset.
Best Practice Functionality Machine Learning algorithm that learns machine behavior without the need for data scientists or the human input.
Practitioners’ Tip: The Importance of Machine Learning Methodology Selection Why is the learning model important? Each specific Machine Learning Methodology approach (manual, Supervised, Unsupervised etc.,) requires differences in levels of internal staff time commitments and infrastructure investments. At the most extreme, manual statistical modelling is an offsite activity that is almost completely non-disruptive. At the other end of the spectrum, to deploy Supervised Machine Learning, an accurate virtual clone of the underlying asset is necessary. The cost differential between solutions based on different models is significant.

#5 Asset Visualization: At a facility level, technicians accessing the user-interface will not be trained in Artificial Intelligence and Big Data. The key considerations when defining this requirement are the visualization of machine behavior and the ability to depict the health of machinery or the entire facility, and take specific action as a result.

Baseline versus Best Practice Functionality

Baseline Functionality Raw statistical information is generated that requires further analysis from big data scientists.
Best Practice Functionality High-quality Business Intelligence (BI) tool with sensor, machine, facility and company-wide view.

The following is an example of a visualization of an Asset Health Indicator from Presenso:

Machine Health Indicator

 

 

 

 

 

 

 

Practitioner’s Tip: Don’t Overlook Visualization Visualization of data generated by Predictive Maintenance solutions is often an overlooked requirement. In terms of business impact, Visualization is one of the most critical components of an Industrial Analytics for Predictive Maintenance solutions. There is a shortage of highly skilled Big Data Scientists and Big Data Engineers that is expected to worsen over the next decade. If analytics generated from a solution are actionable for end users, then the solution needs to provide an intuitive user interface and to summarize the data so that it can be understood by plant and maintenance staff that lack formal training in the discipline of Big Data. Furthermore, the tool should be accessible at both a machine, plant and multi-facility level by stakeholders on the business, IT and operational areas of the organization.

High Priority Non-Functional Requirements for an IIoT Predictive Maintenance Solution

The following Non-Functional Requirement needs to be defined by stakeholders within your organization.

#6 Scalability: Analytics platform must be applicable to a machine or facility of any size. The solution must be able to add assets without a need for any incremental investment in hardware, software or dedicated labor hours.

Baseline versus Best Practice Functionality

Baseline Functionality Predictions are limited to either a specific sensor or asset.
Best Practice Functionality All industrial assets in a facility level or across multiple facilities. complete this, incomplete section.

#7 Performance: The objective for an industrial analytics platform is to provide the production facility with accurate and timely data. Targeted performance measurements of the following will need to be defined:

  • Correct Alerts (True Positives)
  • False Alerts (False Positives)
  • Missed Failures (False Negatives)
  • Recall
  • Precision
  • F-score

The following is a sample scorecard from Presenso that can be used as a template for defining performance requirements:

Template for Performance Requirements

 Practitioner’s Tip: Which stakeholders should define requirements? Predictive Asset Maintenance solutions are a critical component of a plant’s Industry 4.0 strategy. Based on our experience with some of the largest industrial companies, we recommend that you form a cross-functional team that includes the following:

  • Plant Asset Maintenance
  • Plant Engineering
  • Plant Management
  • Headquarters (IT, Finance)

With the Convergence of IT and OT, it is important to include stakeholders from IT. Even if they are not responsible for the business or functional requirements, access to accurate and updated data is a core element of any Industrial Analytics solution. By including representatives of IT in the Planning and Requirements phase, data related issues (such as availability, security and privacy) can be identified upfront and mitigated.  

Other Non-Functional Requirements

The following is a list of non-functional requirements. The specific details will need to be defined by internal stakeholders.

  • Response Time
  • Availability
  • Stability
  • Maintainability
  • Usability

Further Reading: Supply Chain Cyber Security Requirements

We have not provided details about requirements for cyber security because the topic is broad and requires more study. As a starting point, we recommend the National Institute of Standards and Technology Best Practices in Cyber Supply Chain Risk Management.

For more information, please refer to this document.

Additional Resources

  • Considering GE Predix or Siemens MindSphere? How to Evaluate Supervised Machine Learning Solutions for Predictive Asset Maintenance. Full article available here.Considering GE Predix or Siemens Mindsphere? How to Evaluate Supervised Machine Learning Solutions for Predictive Asset MaintenanceConsidering GE Predix or Siemens Mindsphere? How to Evaluate Supervised Machine Learning Solutions for Predictive Asset Maintenance
  • Best Practices for Purchasing IIoT Industrial Analytics Software: How to Conduct a Proof of Concept (POC)Best Practices for Purchasing IIoT Industrial Analytics Software: How to Conduct a Proof of Concept (POC). Full article available here.
  • Big Data Mistakes to Avoid in the Smart Factory. Full article available here.

 

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Eitan Vesely

Eitan Vesely

Formerly a hardware specialist and a support engineer for Applied Materials. Specializing in software-hardware-mechanics interfaces and system overview. Experienced in the field of industrial automation and motion control. Holds a BSc. Mechanical engineering