1. The predictive maintenance solutions of Faststream Technologies use condition monitoring to assess asset performance in real-time. A key element in this process is the Internet of Things (IoT). IoT allows for different assets and systems to connect, work together, share, analyze and make actionable decisions on the data. IoT relies on sensors to capture information, make sense of it and identify any areas that need attention. Some examples of Predictive Maintenance IoT using sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Choosing the correct technique for performing condition monitoring is an important consideration that is best done in consultation with equipment manufacturers and our condition monitoring experts.

WHY PREDICTIVE MAINTENANCE ?

•  Predictive Maintenance would help reduce downtime by a significant amount.
• It would increase the life cycle of assets by providing pre-determined maintenance alerts to reduce actual asset damage.
• It would help increase machine efficiency by making sure assets are functioning in nominal
  conditions.
• Resource Management of spare parts/machine parts would become easier with pre-active maintenance alerts.
• Helps improve supply chain by making efficient maintenance checks.
• Proactive maintenance means all maintenance checks happen proactively avoiding redundancies and complete failures.
  

  Predictive Maintenance on Industry 4.0

  Overview:

  Predictive maintenance for industry 4.0 is a method of preventing asset failure by analyzing production data to identify patterns and predict issues before they happen.

  Factory managers and machine operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime. In addition to consuming unnecessary resources and driving productivity losses, half of all preventive maintenance activities are ineffective.

  It is not a surprise, therefore, that predictive maintenance has quickly emerged as a leading Industry 4.0 use case for manufacturers and asset managers. Implementing industrial IoT technologies to monitor asset health, optimize maintenance schedules, and gaining real-time alerts to operational risks, allows manufacturers to lower service costs, maximize uptime, and improve production throughput.

  How does  Technologies IoT-based predictive maintenance work?

  For predictive maintenance to be carried out on an industrial asset, the following base components are used by  Technologies:

  • • Sensors – data-collecting sensors installed in the physical product or machine
    • Data communication – the communication system that allows data to securely flow between the monitored asset and the central datastore
    • Central data store – the central data hub in which asset data (from OT systems), and business data (from IT systems) are stored, processed, and analyzed; either on-premise or on-cloud
    • Predictive analytics – Predictive analytics algorithms are applied to the aggregated data to recognize patterns and generate insights in the form of dashboards and alerts
    • Root cause analysis – data analysis tools used by maintenance and process engineers to investigate the insights and determine the corrective action to be performed

    The benefits of predictive maintenance with  Technologies:

  • Manufacturers and their customers get a range of business benefits fromTECHNOLOGICS predictive maintenance. The advantages of our Predictive Maintenance include:
  • 1. Reduced maintenance time–Technologies automatic reports for strategic maintenance scheduling and proactive repairs alone reduce maintenance time by 20–50 percent and decrease overall maintenance costs by 5–10 percent. These insights save the manufacturer and their customers time and money.
  • 2. Increased efficiency– Our analytics-driven insights improve OEE (overall equipment effectiveness) by reducing unnecessary maintenance, extend asset life and enable root cause analysis of a system to uncover issues ahead of failure.
  • 3. New revenue streams- Manufacturers can monetize our industrial predictive maintenance by offering analytics-driven services for their customers, including Predictive Maintenance dashboards, optimized maintenance schedules, or a technician dispatch service before parts need replacement. The ability to provide digital services to customers based on data presents an opportunity for recurring revenue streams and a new growth engine for companies.
  • 4. Improved customer satisfaction– Our PDM Solutions send customers automated alerts when parts need to be replaced and suggest timely maintenance services to boost satisfaction and provide a greater measure of predictability.
  • 5. Competitive advantage– Faststream Technologies Predictive maintenance strengthens company branding and value to customers, differentiating their products from the competition and allowing them to provide continuous benefit in-market.
  • • • Our Predictive maintenance tools:
      • • Implementing predictive maintenance requires a baseline of integrated tools.
        • Predictive maintenance tools include an industrial IoT platform to model, simulate, test and deploy the predictive maintenance solution.
        • The tools include industrial data integration and data analytics algorithms to detect patterns in machine data, and root cause analysis tools for investigating the derived insights and determining the corrective action to be taken.

        Difference between preventive and predictive maintenance

        • Manufacturers have been carrying out different forms of preventive and predictive maintenance for years. Understanding the difference between them, however, is critical with the emergence of Industry 4.0.
        • Preventive maintenance depends on visual inspections, followed by routine asset monitoring that provides limited, objective information about the condition of the machine or system. In this process, manufacturers regularly maintain and repair a machine to prevent failure.
        • On the other hand, Predictive Maintenance is data-driven and relies on analytics insights for maintenance and repairs ahead of disruptions in production.

        How are Companies using our IoT-based predictive maintenance tools?

        • Organizations are implementing predictive maintenance analytics in a range of ways, from targeted solutions for a single machine part, to factory-wide deployments for increasing OEE throughout the production line.
        • For machine and parts manufacturers, a relatively common predictive maintenance use case is monitoring and analyzing the condition of a motor to get alerts about its productivity levels, power consumption, health status, and internal wear.
        • Another powerful use case of predictive maintenance is minimizing production defects and reducing waste. Often referred to as Quality 4.0, such implementations can predict when the number of defective products is likely to exceed a threshold percentage and provide the root causes for the expected failure.
        • Manufacturers are also turning to predictive maintenance for Factory 4.0, or a connected factory, by installing sensors in machines, workstations, and other designated sites such as the HVAC, security cameras, or worker equipment, to predict issues across the factory floor.

        The approaches to our IoT predictive maintenance

        Rule-based predictive maintenance

      • It is also referred to as condition monitoring, rule-based predictive maintenance relies on sensors to continuously collect data about assets, and sends alerts according to predefined rules, including when a specified threshold has been reached. With rule-based analytics, product teams work alongside engineering and customer service departments to establish causes or contributing factors to their machine’s failure. Once common reasons for a product or part failure are established, manufacturers can build a virtual model of their connected system. Here they define product use cases, with “if-this-then-that” rules which describe the behaviors and inter-dependencies between the various IoT system components. For example, if temperature and rotation speed are above certain predefined levels, the system will send an alert to an operator dashboard, to address the issue ahead of failure. These rules provide a level of automated, predictive maintenance, but they are still dependent on a product team’s understanding of what parts or environmental elements require measuring. The condition monitoring dashboards can be integrated with insight from machine learning to provide a visually understandable heatmap of asset conditions in real-time.
      • Predictive Maintenance with AI
      • Industrial artificial intelligence can be applied to predictive maintenance and many other use cases in the manufacturing industry, and although we are just in the beginning of exploiting this technology, there are already many facilities benefiting from industrial AI.AI is perfectly suited to predictive maintenance. It offers a host of techniques to analyze the huge amounts of data collected from the manufacturing process and deliver actionable insights to reach and sustain manufacturing excellence. These techniques are referred to as Machine Learning algorithms.
      • Applying Machine Learning to predict asset failure
      • Predictive maintenance with machine learning looks at large sets of historical or test data, combined with tailored machine-learning (ML) algorithms, to run different scenarios and predict what will go wrong, and when.
      • Predictive Maintenance ML Algorithms
      • Advanced AI algorithms learn a machine’s normal data behavior and use this as a baseline to identify and alert to deviations in real-time. The algorithms required for machine learning must analyze input (historical or a training set of data) and output data (the desired result). A machine monitoring system includes input on a range of factors from temperature to pressure and engine speed. The output is the variable in question – a warning of a future system or part failure. The system will then be able to predict when a breakdown is likely to occur. There are two main approaches to AI and machine learning for predictive analytics – supervised and unsupervised machine learning – each is relevant for a different scenario and depends on the availability of sufficient historical training data and the frequency of asset failure.