Industrial Internet of Things (IIoT) is a specific term used to integrate the Cyber-Physical Systems (CPS). It is applied in the monitoring and maintenance space of diverse industries that include manufacturing, transportation, and utilities. By introducing the IoT in industrial setup led to the development of efficient and reliable Industrial Automation and Control Systems (IACS) referred to as Operational Technology (OT).

A CPS is a conventional system (software, hardware, communication interface, IoT sensors, edge, and cloud computing) that is primarily centralized if not distributed. CPS plays a core role in Industry 4.0, as an integrated physical asset between the machines and the computing resources. An extra unit of intelligent software (Artificial Intelligence – AI) will potentially add value in IoT based monitoring and maintenance systems.

The maintenance cost of an industrial asset is significant in total operating costs in the production line. It is well documented that maintenance cost ranges from 15 – 60 % of the cost of goods produced. The poor maintenance could also lead to downtime in the peak production hours. The IIoT is evolving in a large phase that includes the greater connectivity to the industrial systems.

In Industry, IoT network infrastructure is the main source of critical data collection from various sensors such as pressure sensors, thermocouples, Resistance Temperature Detectors (RTDs), etc. Based on its action some sensors work passively that does not involve any perturbation in industrial asset maintenance. While other sets of sensors (e.g. RTDs) use the method of injecting signals into the equipment to measure its response for maintenance purposes.

Generally, there are four types of maintenance technique used and was listed by ABB (is a Swiss-Swedish multinational corporation). They are,

  • Corrective maintenance: This method of maintaining industrial machinery comes into action after the faulty state of assets has occurred. It is a cost-effective approach that cannot withstand or maintain the sudden failure of the components.
  • Preventive maintenance: This approach is based on time and usage triggers to increase the industrial asset lifetime. It is a routine based maintenance strategy planned between the predetermined time intervals.
  • Risk-based maintenance (RBM): This method is focused on the industrial environment where the machinery resources are limited. This follows the principle to balance the Probability of Failure (PoF) and consequences of failure (CoF) of an industrial asset to reduce the high risk and downtime. Based on the sensor data extracted from these machinery the asset that shows the abnormal behavior will be refurbished or replaced. This method ensures the high level of reliability, safety, and efficiency of an industrial asset.
  • Condition-based maintenance (CBM): CBM in other terms called as “on-line monitoring” or “predictive maintenance”. The maintenance depends on the current condition of the assets which was determined by visual inspection, tests, and real-time performance data gathered from embedded sensors. It is one of the powerful and widely used approaches, nowadays this technique is automated using the high-level advanced signal processing technologies powered by Artificial Intelligence (AI) algorithms. This technique with big data and machine learning algorithms could possibly detect the occurrence of serious faults in real-time and in near future.

According to IBM analytics, it is estimated that the predictive maintenance market size is growing from USD $3.0 billion in 2019 to USD $10.7 billion by 2024.

To manage and maintain the complex industrial process and asset, an automated and efficient cloud-based PdM platform is in need. This dedicated service of maintenance and management platform for industry 4.0 was provided by Faststream Technologies. This solution from Faststream Technology based on condition-monitoring uses IIoT as a key element in its technical stack.

Predictive Maintenance (PdM) that rely on the condition-based (CBPdM) strategy is more efficient and productive than a conventional PdM. PdM with dynamic decision thresholds and optimized Digital Signal Processing (DSP) algorithms have emerged as a promising tool to minimize the asset downtime and associated management cost. Condition Monitoring System (CMS) is the base for PdM that in collaboration with several Machine Learning (ML) pipeline in parallel could early forecast the potential hazards and future faults.

Industrial IoT when combined with the Computerized Maintenance Management System (CMMS) opens new possibilities in asset maintenance operations. CMMS is a computer software tool developed to optimize the task of maintenance digitally. IoT act as a vast source of the real-time data connection. This real-time big data is acquired using Supervisory Control and Data Acquisition (SCADA). Collected data is then connected to the CMMS system to monitor and keep track of the asset’s health in real-time. IoT here plays as a mediator that connect, share, analyze, monitor, and take decision continually for data from industrial asset and system.

CMMS software provided by Faststream Technologies uses various maintenance triggers such as break down trigger, time trigger, Usage trigger, event trigger, Condition trigger, and Meter trigger to notify the maintenance alert needed for an asset to the technician.

To find and prevent the potential failure in manufacturing equipment or asset in ahead of time Faststream Technologies platform needs some technical stack as follows:

  • Intelligent IoT sensors – Sensors in IIoT play a vital role in the link between the physical systems. Faststream Technologies supports collecting various forms of data (thermal image, vibration analysis, acoustic signals, equipment observation, etc). All these real-time data are then connected to the IIoT network/gateway using the low-level communication protocols such as Serial Peripheral Interface (SPI), Inter-Integrated Circuit (I2C), RS-232, etc. These protocols are build using the physical circuit such as Universal Asynchronous Receiver or Transmitter (UART) and General-Purpose Interface Bus (GPIB).
  • IoT gateway – IIoT gateways are developed to provide the vertical service between the IoT sensors and the cloud server backend. In a more complex industrial environment, there is even a need for intelligent IoT gateway (field gateway) for edge computing.
  • Cloud Services – Since the cloud services (Microsoft Azure, Amazon, Thinkspace, etc) are evolving in a large phase cloud-based CMMS platform hosting has several advantages. Various cloud services such as centralized big data warehouse, Serverless computing, analytics report, etc.
  • Application interface – Web or mobile API acts as an interface or control unit for displaying the customizable report regarding the asset health and monitoring condition.
Technical stack for Predictive Maintenance end-to-end platform

Faststream Tech PdM platform with the universal model is used to develop an advanced predictive maintenance solution for diverse industries such as leading oil and gas production companies, electric power industry, railways, petrochemical companies, mechanical engineering companies, logistics, etc. Today in the competitive industrial world PdM platform by Faststream Technologies have a mandatory role because of several beneficial factors such as,

  • Reduced Maintenance Time – PdM helps to eliminate 20 % – 50 % of the time-based maintenance routine and reduces the maintenance cost by 5% – 10%. Faststream tech provides remote online and offline PdM services that reduce the overall time spent on maintenance tasks.
  • Increased efficiency and productivity – With continuous monitoring in real-time it offers the possibility of increased product efficiency optimizing the maintenance cost. Along with the AI technology it enables a root cause analysis to predict the failure in advance. With the increased productivity it brings customer satisfaction.
  • Revenue streams – Original Equipment Manufacturers (OEM) can generate revenue by providing our services (predictive analytics, dashboard, or technician dispatch service before the fault occurs) to the customers.
  • Competitive advantage – Faststream Technologies PdM solution has the ability to integrate new decision rules and adapting to diverse kinds of industrial machinery brings a big and strong challenge to the competitors in the market.
  • Increased Safety – With the right tools, the right strategy, and huge data the hidden challenge of workers’ safety is taken care of. With the powerful Machine Learning and Ai algorithms, early detection of an equipment failure is highly possible. This safety feature by Faststream technologies enables it to provide a safe environment for the workers.

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