Artificial intelligence tools can aid sensor systems
Ambient intelligence has been promoted for the last decade as a vision of people working easily in digitally controlled environments in which the electronics can anticipate their behavior and respond to their presence. The concept of ambient intelligence is for seamless interaction between people and sensor systems to meet actual and anticipated needs.
Use in industry has been limited, but new, more intelligent and more interactive systems are at the research stage. From the perspective of sensor systems, a less human and more system-centered definition of ambient intelligence needs to be considered. Modern sensor concepts tend to be human-centered approaches so that the application of ambient intelligence technologies in a combination with knowledge management may be a promising approach. Many research issues still have to be resolved to bring the ambient intelligence technology to industrial sectors, such as robust, reliable (wireless) sensors and context-sensitivity, intelligent user interfaces, safety, security, and so forth.
Ambient intelligence information and knowledge gathered from sensors within an environment represents an untapped resource for optimization processes and for possibilities to provide more efficient services. The introduction of ambient intelligence technologies is still in an initial phase. However, it is promising to bring advantages in flexibility, reconfigurability, and reliability. At the same time, prices of sensors and tags are decreasing. Development and implementation of new concepts based on ambient intelligence systems in the mid- and long-term are likely. A large number of industrial companies will probably introduce different ambient intelligence technologies to the shop-floor.
On the other hand, vendors of sensors will need to equip their products with additional ambient intelligence features and utilize the advantages of ambient intelligence integrated sensors within the environment to provide new functionalities (for example: self-configuration, context-sensitivity, etc.) and improve performances of their products.
[More information is in 87, 88.]
AI applications, University of Portsmouth
AI tools are being applied at the University of Portsmouth to assist industry in the adoption of artificial intelligence for use with sensor systems.
Monitoring and controlling machinery: Simple rules are being investigated that modify pre-planned paths and improve gross robot motions associated with pick-and-place assembly tasks,  and rules to predict terrain contours are being developed using a feed-forward neural network.  Case-based reasoning is being applied to reuse programs (or parts of programs) to automatically program sensor arrays. The combined work is already showing that automatic programming and re-programming may help to introduce environmental sensors into smaller and medium enterprises. Other projects are using simple expert systems to improve the use of sensor data in tele-operation applications. [91, 92, 93]
Process monitoring and control: An expert system is being developed to assist in process control and to enhance the implementation of statistical process control. A bespoke expert system uses a hybrid rule-based and pseudo object-oriented method of representing standard statistical process control knowledge and process-specific diagnostic knowledge. The amount of knowledge from sensor arrays and sensor systems can be large, which justifies the use of a knowledge-based systems approach. The system is being enhanced by integrating a neural network module with the expert system modules to detect any abnormal patterns. 
Monitoring sensor arrays: A system has been created to monitor sensors in a high recirculation airlift reactor (a process to produce clean water). [8, 18, 95] Reactors can be at the edge of stability, and that requires accurate interpretation of real-time sensor data from sensors, such as: flow rate, air input, pressure, etc. A second system is interpreting data from ultrasonic sensor arrays on tele-operated mobile robots and on wheelchairs. [23, 24, 33, 34]
Fuzzy monitoring and control: A robotic welding system is being created that uses image processing techniques and a computer-aided design (CAD) model to provide information to a multi-intelligent decision module.  The system uses a combination of techniques to suggest weld requirements. These suggestions are evaluated, decisions are made, and then weld parameters are sent to a program generator. The status of the welding process is difficult to monitor because of the intense disturbance during the process. Other work is using multiple sensors to obtain information about the process. Fuzzy measurement and fuzzy integral methods are being investigated to fuse extracted signal features in order to predict the penetration status of the welding process.
Neural-network-based product inspection: Two projects are using neural networks for product inspection: one is recognizing shipbuilding parts and a second is using cameras to detect and classify defects. Neural networks are useful for these types of applications because of the common difficulty in precisely describing various types of defects and differences. The neural networks can learn the classification task automatically from examples.
The first system is managing to recognize shipbuilding parts using artificial neural networks and Fourier descriptors.  Improvements have been made to a pattern recognition system for recognizing shipbuilding parts.  This has been achieved by using a new, simple and accurate corner-finder. The new system initially finds corners in an edge-detected image of a part and uses that new information to extract Fourier descriptors to feed into a neural network to make decisions about shapes. Using an all-or-nothing accuracy measure, the new systems have achieved an improvement over other systems.
A second intelligent inspection system has been built that consists of cameras connected to a computer that implements neural-network-based algorithms for detecting and classifying defects. Outputs from the network indicate the type of defect. Initial investigation suggests that the accuracy of defect classification is good (in excess of 85%) and faster than manual inspection. The system is also used to detect defective parts with a high accuracy (almost 100%).
Genetic algorithms to create an ergonomic workplace layout: A genetic algorithm for deciding where to place sensors in a work cell is being developed. The layout produced by the program will be such that the most frequently needed sensors are prioritized. A genetic algorithm is suitable for this optimization problem because it can readily accommodate multiple constraints.
Ambient intelligence to improve energy efficiency: Ambient intelligence and knowledge management technologies are being used to optimize the energy efficiency of manufacturing units.  This benefits both the company and the environment as the carbon footprint is reduced. Different measuring systems are being applied to monitor energy use.  Ambient data provide the opportunity to have detailed information on the performance of a manufacturing unit.  Knowledge management facilitates processing this information and advises on actions to minimize energy usage but maintain production. Existing energy consumption data from standard measurements is being complemented by ambient intelligence related measurements (from interactions of human operators and machines/processes and smart tags) as well as process related measurements (manufacturing line temperatures, line pressure, production rate) and knowledge gathered within the manufacturing assembly unit. This is fed to a service oriented architecture system. Figure 3 shows an experimental system to use ambient intelligence to improve energy efficiency.
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Before the calendar turned, 2016 already had the makings of a pivotal year for manufacturing, and for the world.
There were the big events for the year, including the United States as Partner Country at Hannover Messe in April and the 2016 International Manufacturing Technology Show in Chicago in September. There's also the matter of the U.S. presidential elections in November, which promise to shape policy in manufacturing for years to come.
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