**2.2 IoT and IIoT implementations**

Many organizations use manual and off-line inspection tools for their reliability and maintainability programs while IIoT provides an opportunity to perform these tasks on-line in real-time time. IoT alone is projected to deliver between \$1.9 and \$4.7 trillion of economic value by 2025. The IIoT for asset monitoring is expected to produce \$200-\$500 billion in economic value by 2025 [13]. These technologies enable machine health monitoring (or also referred as condition monitoring) and predictive maintenance to optimize maintenance processes and improve operating costs. This type of monitoring is expected to help manufacturers to optimize their operating costs by predicting the failure of critical machines and their components to achieve high efficiency and reliability.

According to one market report the global machine health monitoring market size, driven by on-premises deployment, is estimated to reach USD 3.9 billion by 2025 [14]. On-premises application development give organizations control over their data and systems to protect the critical information. Whereas deployment on a remote cloud has its advantages and disadvantages related to hardware, software, deployment, and maintenance costs. Another factor that needs to be considered carefully is storage capacity at the local and remote server sites. Cloud-based deployments do provide organizations with enhanced accessibility and scalability, 24/7 service speed, and IT security measures that cannot be implemented due to lack of resources at the local site. In part the growth of this market is being driven by the availability of secure cloud platforms. An overall IoT/IIoT ecosystem with elements shown in **Figure 1** would be required. This type of ecosystem has been established at the SEPT Learning Factory to demonstrate these technologies [15].

Online machine health monitoring systems can be implemented for critical equipment, such as motors, turbines, blowers, pumps, and compressors, that have an immediate impact on the productivity of plants as well as human and machine safety, and the environment. Current monitoring systems include a sequence of sensors permanently mounted on the critical machines for sensing. The sensors are connected to microcontrollers, single board computers, and/or PLCs, and generated data can be sent to a central server of a plant or to an outside cloud platform such as

**Figure 1.** *IoT/IIoT ecosystem overview.*

PubNub, Google, Amazon, and ThingsBoard etc. The sensor data is sent to the plant operators through either a wireless network or a cabled network and displayed on monitors. This can be accomplished in two manners as depicted in **Figures 2** and **3**. UniS is the UniSphere 1 local platform at SEPT hosting MQTT broker, **Figure 4**, as well DDS server.

**Figure 2.** *A typical IoT model used in SEPT learning factory.*

**Figure 3.**

*An IIoT model used in SEPT learning factory.*

**Figure 4.** *MQTT broker local and remote servers.*
