Machine to Machine (M2M) refers to direct communication between devices using any communications channel, including wired and wireless. M2M communication can include industrial instrumentation, enabling a sensor or meter to communicate the data it records (such as temperature, inventory level, etc.) to application software that can use it (for example, adjusting an industrial process based on temperature or placing orders to replenish inventory). Such communication was originally accomplished by having a remote network of machines relay information back to a central hub for analysis, which would then be rerouted into a system like a personal computer.
More recent machine to machine communication has changed into a system of networks that transmits data to personal appliances. The expansion of IP networks around the world has made machine to machine communication quicker and easier while using less power. These networks also allow new business opportunities for consumers and suppliers.
While many machines still use landlines (POTS, DSL, cable) to connect to the IP network, Primal and its telecom partners are using SMS for M2M messaging with minimal power and bandwidth requirements.
Wireless networks that are all interconnected can serve to improve production and efficiency in various areas, including machinery that works on building cars and on letting the developers of products know when certain products need to be taken in for maintenance and for what reason. Such information serves to streamline products that consumers buy and works to keep them all working at highest efficiency.
Telematics and in-vehicle entertainment is an area of focus for machine to machine developers. Recent examples include Ford Motor Company, which has teamed with AT& to wirelessly connect Ford Focus Electric with an embedded wireless connection and dedicated app that includes the ability for the owner to monitor and control vehicle charge settings, plan single- or multiple-stop journeys, locate charging stations, pre-heat or cool the car. In 2011, Audi partnered with T-Mobile and RACO Wireless to offer Audi Connect. Audi Connect allows users access to news, weather, and fuel prices while turning the vehicle into a secure mobile WiFi hotspot, allowing passengers access to the Internet.
At Primal and iMetrik we are using SMS and the Primal Cloud to track telematics of cars. How fast they are, where they are, and what their locations are, can all be determined with a modem and SMS. Efficient data transfer with GPS and SMS allows low cost tracking.
M2M wireless networks can serve to improve the production and efficiency of machines, to enhance the reliability and safety of complex systems, and to promote the life-cycle management for key assets and products. By applying Prognostic and Health Management (PHM) techniques in machine networks, the following goals can be achieved or improved:
- • Near-zero downtime performance of machines and system
- • Health management of a fleet of similar machines
The application of intelligent analysis tools and Device-to-Business (D2B) TM informatics platform form the basis of an e-maintenance machine network that can lead to near-zero downtime performance of machines and systems. The e-maintenance machine network provides integration between the factory floor system and e-business system, and thus enables the real time decision making in terms of near-zero downtime, reducing uncertainties and improved system performance. In addition, with the help of highly interconnected machine networks and advance intelligent analysis tools, several novel maintenance types are made possible nowadays. For instance, the distant maintenance without dispatching engineers on-site, the online maintenance without shutting down the operating machines or systems, and the predictive maintenance before a machine failure become catastrophic.
Another application of a M2M network is in the health management for a fleet of similar machines using clustering approach. This method was introduced to address the challenge of developing fault detection models for applications with non-stationary operating regimes or with incomplete data. The overall methodology consists of two stages: 1) Fleet Clustering to group similar machines for sound comparison; 2) Local Cluster Fault Detection to evaluate the similarity of individual machines to the fleet features. The purpose of fleet clustering is to aggregate working units with similar configurations or working conditions into a group for sound comparison and subsequently create local fault detection models when global models cannot be established. Within the framework of peer to peer comparison methodology, the machine to machine network is crucial to ensure the instantaneous information share between different working units and thus form the basis of fleet level health management technology.
The fleet level health management using clustering approach was patented for its application in wind turbine health monitoring after validated in a wind turbine fleet of three distributed wind farms. Different from other industrial devices with fixed or static regimes, wind turbines’ operating condition is greatly dictated by wind speed and other ambient factors. Even though the multi-modeling methodology can be applicable in this scenario, the number of wind turbines in a wind farm is almost infinite and may not present itself as a practical solution. Instead, by leveraging on data generated from other similar turbines in the network, this problem can be properly solved and local fault detection models can be effectively built. The results of wind turbine fleet level health management reported is demonstrated in the effectiveness of applying a cluster-based fault detection methodology in the wind turbine networks.
Fault detection for a horde of industrial robots experiences similar difficulties as lack of fault detection models and dynamic operating condition. Industrial robots are crucial in automotive manufacturing and perform different tasks as welding, material handling, painting, etc. In this scenario, robotic maintenance becomes critical to ensure continuous production and avoid downtime. Historically, the fault detection models for all the industrial robots are trained similarly. Critical model parameters like training samples, components, and alarming limits are set the same for all the units regardless of their different functionalities. Even though these identical fault detection models can effectively identify faults sometimes, numerous false alarms discourage users from trusting the reliability of the system. However, within a machine network, industrial robots with similar tasks or working regimes can be grouped together; the abnormal units in a cluster can then be prioritized for maintenance via training based or instantaneous comparison. This peer-to-peer comparison methodology inside a machine network could improve the fault detection accuracy significantly.
Primal continues to push the envelope in 2018 with M2M; directly with solution providers or with carriers who understand this business, so they will continue to flourish and grow.