Artificial intelligence tools can aid sensor systems

At least seven artificial intelligence (AI) tools can be useful when applied to sensor systems: knowledge-based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case-based reasoning, and ambient-intelligence. See diagrams.

10/30/2013


Figure 1 shows a case-based reasoning (CBR) system. As with rule-based systems, CBR systems are good at representing knowledge in a way that is clear to humans; however, CBR systems also have the ability to learn from past examples by generating additionaSeven artificial intelligence (AI) tools are reviewed that have proved to be useful with sensor systems. They are: knowledge-based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case-based reasoning, and ambient-intelligence. Each AI tool is outlined, together with some examples of its use with sensor systems. Applications of these tools within sensor systems have become more widespread due to the power and affordability of present-day computers. Many new sensor applications may emerge, and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed.

The tools and methods reviewed here have minimal computation complexity and can be implemented with small sensor systems, single sensors, or system arrays with low-capability microcontrollers. The appropriate deployment of the new AI tools will contribute to the creation of more competitive sensor systems and applications. Other technological developments in AI that will impact sensor systems include data mining techniques, multi-agent systems, and distributed self-organizing systems. Ambient sensing involves integrating many microelectronic processors and sensors into everyday objects to make them “smart.” They can explore their environment, communicate with other smart things, and interact with humans. Advice provided aims to help users cope with their tasks in intuitive ways, but the repercussion of such integration into our lives is difficult to predict. Using ambient intelligence and a mix of AI tools is an effort to use the best of each technology. The concepts are generically applicable across industrial processes, and this research is intended to show that the concepts work in practice.

Creating smarter sensor systems

Sensor systems can be improved using artificial intelligence (AI). [1] AI emerged as a computer science discipline in the mid 1950s, [2,3] and it has produced a number of powerful tools that are useful in sensor systems for automatically solving problems that would normally require human intelligence. Seven such tools are here: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, case-based reasoning, and ambient-intelligence.

AI systems have been improving, [4] and new advances in machine intelligence are creating seamless interactions between people and digital sensor systems. Although the introduction of AI into industry has been slow, it promises to bring improvements in flexibility, reconfigurability, and reliability. New machine systems are exceeding human performance in increasing numbers of tasks. As they merge with us more intimately, and we combine our brain power with computer capacity to deliberate, analyze, deduce, communicate, and invent, then we may be on the threshold of a new age of machine intelligence. [5]

AI (or machine intelligence) combines a wide variety of advanced technologies to give machines an ability to learn, adapt, make decisions, and display new behaviors. [6] This is achieved using technologies such as neural networks, [7] expert systems, [8,9] self-organizing maps, [10] fuzzy logic, [11] and genetic algorithms, [12] and that machine intelligence technology has been developed through its application to many areas where sensor information has needed to be interpreted and processed, for example:

  • Assembly [1, 14]
  • Biosensors [13]
  • Building modeling [16]
  • Computer vision [17]
  • Cutting tool diagnosis [30]
  • Environmental engineering [18]
  • Force sensing [31]
  • Health monitoring [29]
  • Human-computer interaction [19, 20]
  • Internet use [21, 22]
  • Laser milling [15]
  • Maintenance and inspection [25, 26]
  • Powered assistance [23, 24]
  • Robotics [27, 28]
  • Sensor networks [32]
  • Teleoperation. [33, 34]

These developments in machine intelligence are being introduced into ever more complex sensor systems. The click of a mouse, the flick of a switch, or the thought of a brain might convert almost any sensor data to information and transport it to you. Recent examples of this research work are provided, which include work at the University of Portsmouth. Seven areas where AI can help sensor systems follow.

1. Knowledge-based systems

Knowledge-based (or expert) systems are computer programs embodying knowledge about a domain for solving problems related to that domain. [2] An expert system usually has two main elements, a knowledge base and an inference mechanism. The knowledge base contains domain knowledge which may be expressed as a combination of ‘IF–THEN' rules, factual statements, frames, objects, procedures, and cases. An inference mechanism manipulates stored knowledge to produce solutions to problems. Knowledge manipulation methods include using inheritance and constraints (in a frame-based or object-oriented expert system), retrieval and adaptation of case examples (in case-based systems), and the application of inference rules (in rule-based systems), according to some control procedure (forward or backward chaining) and search strategy (depth or breadth first). [1]

A rule-based system describes knowledge of a system in terms of IF… THEN... ELSE. Specific knowledge can be used to make decisions. These systems are good at representing knowledge and decisions in a way that is understandable to humans. Due to the rigid rule-base structure they are less good at handling uncertainty and are poor at handling imprecision. A typical rule-based system has four basic components: a list of rules or rule base, which is a specific type of knowledge base; an inference engine [35, 36] or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base; temporary working memory; and a user interface or other connection to the outside world through which input and output signals are received and sent. [1]

The concept in case-based reasoning is to adapt solutions from previous problems to current problems. These solutions are stored in a database and can represent the experience of human specialists. When a problem occurs that a system has not experienced, it compares with previous cases and selects one that is closest to the current problem. It then acts upon the solution given and updates the database depending upon the success or failure of the action. [37] Case-based reasoning systems are often considered to be an extension of rule-based systems. They are good at representing knowledge in a way that is clear to humans, but they also have the ability to learn from past examples by generating additional new cases.

2. Case-based reasoning 

Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process:

1) Retrieve: Given a target problem, retrieve cases from memory that are relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived.

2) Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation.

3) Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise.

4) Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory.

Critics argue that it is an approach that accepts anecdotal evidence as its main operating principle. Without statistically relevant data for backing and implicit generalization, there is no guarantee that the generalization is correct. However, all inductive reasoning where data is too scarce for statistical relevance is inherently based on anecdotal evidence. [1]

The concept in case-based reasoning (CBR) is to adapt solutions from previous problems to current problems. These solutions are stored in a database and represent the experience of human specialists. When a problem occurs that a system has not experienced, it compares with previous cases and selects one closest to the current problem. It then acts upon the solution given and updates the database depending upon the success or failure of the action.

CBR systems are often considered to be an extension of rule-based systems. [1] As with rule-based systems, CBR systems are good at representing knowledge in a way that is clear to humans; however, CBR systems also have the ability to learn from past examples by generating additional new cases. Figure 1 shows a CBR system.

Many expert systems are developed using programs known as “shells,” which are ready-made expert systems complete with inferencing and knowledge storage facilities but without the domain knowledge. Some sophisticated expert systems are constructed with the help of “development environments.” The latter are more flexible than shells in that they also provide means for users to implement their own inferencing and knowledge representation methods. [Some details about expert systems shells and development environments are in Ref. 40.]

Expert systems are probably the most mature among tools mentioned here, with many commercial shells and development tools available to facilitate their construction. Consequently, once the domain knowledge to be incorporated in an expert system has been extracted, the process of building the system is relatively simple. The ease with which expert systems can be developed has led to a large number of applications of the tool. In sensor systems, applications can be found for a variety of tasks, including selection of sensor inputs, interpreting signals, condition monitoring, fault diagnosis, machine and process control, machine design, process planning, production scheduling, and system configuring. Some examples of specific tasks undertaken by expert systems are:

  • Assembly [44]
  • Automatic programming [41]
  • Controlling intelligent complex vehicles [42]
  • Planning inspection [46]
  • Predicting risk of disease [48]
  • Selecting tools and machining strategies [45]
  • Sequence planning [43]
  • Controlling plant growth. [47]

[More information on the technology of expert systems is in 3, 49.]


<< First < Previous 1 2 3 4 5 Next > Last >>

No comments
The Top Plant program honors outstanding manufacturing facilities in North America. View the 2013 Top Plant.
The Product of the Year program recognizes products newly released in the manufacturing industries.
The Leaders Under 40 program features outstanding young people who are making a difference in manufacturing. View the 2013 Leaders here.
The new control room: It's got all the bells and whistles - and alarms, too; Remote maintenance; Specifying VFDs
2014 forecast issue: To serve and to manufacture - Veterans will bring skill and discipline to the plant floor if we can find a way to get them there.
2013 Top Plant: Lincoln Electric Company, Cleveland, Ohio
Case Study Database

Case Study Database

Get more exposure for your case study by uploading it to the Plant Engineering case study database, where end-users can identify relevant solutions and explore what the experts are doing to effectively implement a variety of technology and productivity related projects.

These case studies provide examples of how knowledgeable solution providers have used technology, processes and people to create effective and successful implementations in real-world situations. Case studies can be completed by filling out a simple online form where you can outline the project title, abstract, and full story in 1500 words or less; upload photos, videos and a logo.

Click here to visit the Case Study Database and upload your case study.

Bring focus to PLC programming: 5 things to avoid in putting your system together; Managing the DCS upgrade; PLM upgrade: a step-by-step approach
Balancing the bagging triangle; PID tuning improves process efficiency; Standardizing control room HMIs
Commissioning electrical systems in mission critical facilities; Anticipating the Smart Grid; Mitigating arc flash hazards in medium-voltage switchgear; Comparing generator sizing software

Annual Salary Survey

Participate in the 2013 Salary Survey

In a year when manufacturing continued to lead the economic rebound, it makes sense that plant manager bonuses rebounded. Plant Engineering’s annual Salary Survey shows both wages and bonuses rose in 2012 after a retreat the year before.

Average salary across all job titles for plant floor management rose 3.5% to $95,446, and bonus compensation jumped to $15,162, a 4.2% increase from the 2010 level and double the 2011 total, which showed a sharp drop in bonus.

2012 Salary Survey Analysis

2012 Salary Survey Results

Maintenance and reliability tips and best practices from the maintenance and reliability coaches at Allied Reliability Group.
The One Voice for Manufacturing blog reports on federal public policy issues impacting the manufacturing sector. One Voice is a joint effort by the National Tooling and Machining...
The Society for Maintenance and Reliability Professionals an organization devoted...
Join this ongoing discussion of machine guarding topics, including solutions assessments, regulatory compliance, gap analysis...
IMS Research, recently acquired by IHS Inc., is a leading independent supplier of market research and consultancy to the global electronics industry.
Maintenance is not optional in manufacturing. It’s a profit center, driving productivity and uptime while reducing overall repair costs.
The Lachance on CMMS blog is about current maintenance topics. Blogger Paul Lachance is president and chief technology officer for Smartware Group.