Signal processing for automatic supervision

Consumers are now demanding products that are reasonably priced and reliable. As a result, manufacturers have to develop manufacturing systems that are flexible and can accommodate a variety of products promising high performance. Performance demands precision and complexity to different degrees and increased attention to monitoring devices during production.

The expense of automating manufacturing operations is high enough and demands to monitor the process. Thus there is also a great demand for monitoring systems to ensure the safe and efficient performance of these systems during operation. If we add to this the great diversity of materials, operating conditions and tooling it is highly likely that malfunctions will occur. In the absence of good models of these processes to predict performance, sensors have been utilized in these systems to reduce unexpected malfunctions. Sensing technology will play an important role in the development of future factory systems. As pointed above, both processing and systems conditions must be monitored to ensure optimum performance. There are three major strategies at present used to implementing process monitoring and control using time-critical and non-time-critical situation sensing techniques. These are:

1. Open-loop monitoring systems that measure some conditions of the machine tool or process and then display or activate an alarm to initiate human intervention;

2. Open-loop diagnostic systems that attempt to determine a functional or casual relationship between a machine failure and its cause; and

3. Closed-loop adaptive control systems that automatically adapt machining conditions to changes in the process environment according to pre-determined strategies.

Improvements have been suggested to make these systems more effective. One of the suggestions is related to improved sensors and sensor data-handling techniques.

In the nearest future compact multiple-purpose sensors and sensors for "ambiguity (неопределенный) factors" will be developed. It is also clear that more than one sensor will be utilized to improve the reliability of the monitoring systems. There have been strategies developed using multiple sensors in the past but these are essentially sets of sensor systems operating independently of each other, each sensor relating to a different phenomenon. Of interest here is the integration of sensors to provide an environment that uses the combined information from a number of sensors to render a decision on the state of a manufacturing process, tool or machine. This is referred to a sensor fusion.

More recently some researches in multi-sensor systems for process monitoring have been made. In this case a variety of sensors are used to provide a range of process characteristics with the goal (цель) of ensuring a higher reliability. This multi-sensor approach then requires more attention to feature extraction, information integration and decision-making in real time to be effective. Sensing systems for manufacturing processes must balance a number of options if they are to be effective. For example:

1) Does the nature of application require a sensor response to a detected phenomenon that is slow or fast? This will determine the type of sensor to be used and, most significantly, the amount of digital signal processing/hardware needed to meet the demand.

2) Does the sensing techniques require that the sensor be in contact or not in contact with he component under surveillance (наблюдение)? This will determine the type of sensor that can be employed as well as the degree of modification of the machine, tooling and process needed to implement the sensor.

3) Does the application require a direct or indirect sensor measurement? This determines the type, location, required signal treatment and performance.

4) Does the measurement made by the sensor need to be done in real time, i.e. during the process, or can it be done before and after the process, or perhaps between steps in the process? This, along with the time response of the sensor, will dictate the type of sensor that can be used.

There have been many sensors methodologies suggested for the process, tool or machine monitoring in the manufacturing environment. But much additional research is needed to make these techniques useful. Often the addition of enhanced signal processing methodologies can make these sensing techniques more reliable.

Achieving untended manufacturing is the biggest obtacle confronting the development of computer integrated manufacturing or, on the smaller scale, flexible manufacturing systems. Sensor function to collect information for the evaluation of the performance for the system and its consistency (согласованность) with analytical predictions. Sensors must often operate in hostile environments, and existing sensors are either limited in accuracy, reliability, range or response, or are inappropriate for some of the phenomena under observation.

Interested in developing a capability for untended manufacturing systems is growing along with the implementation of advanced flexible manufacturing processes and minimize the time lost due to repair or correction of unexpected failures in the system, new sensing methodologies and sensor-based control schemes are being proposed. One of the major blocks to implementation of true untended manufacturing is the lack of suitable sensors for process monitoring. In spite of many sensing technologies existing today, few totally untended operations exist.

It seems reasonable that, to be effective in untended manufacturing, combinations of sensors will be needed to provide corroborative (подтверждающий) information on the state of the manufacturing operation. This often includes integration with other sensors and the co-processing of data from several basic different sources, since one sensor alone may be unable to determine the process state adequately. This will require efficient methodologies for the integration of sensor information. Easily available information on the operation of the process (such as speeds, feeds etc. in machining) will need to be considered as well.

 

The philosophy of implementation of any sensing methodology for diagnostics or process monitoring can be divided into two simple approaches. In the first, one uses a sensing techniques for which the output shows some relationship to the characteristics of the process. After determining the sensor output and behaviour for the "normal" machine operation or process, one observes the behaviour of the signal unit, which deviates (отклоняться) from the normal position and thus indicates a problem. In the second approach, one attempts to determine a model linking the sensor output to the process mechanics and then, with sensor information, use this model to predict the behaviour of the process. Both methods are useful according to circumstances. The first is, perhaps, the more straightforward (простой) but liable (способный) to misinterpretation if some changes in the process occurs that was not foreseen. To ensure against this type of misinterpretation, intelligence has been added to the sensors to give sophistication to the feature extraction and decision-making process. Intelligent sensing systems have been associated with robot systems operating in unstructured environments. This has been motivated by the need to integrate multiple sensors for flexibility in control of the robot. In these applications, information from only one sensor is generally insufficient to allow complete specification of the environment for task planning and execution. Multiple sensors are often employed for object location and recognition, for example, and use camera, infra-red, ultrasonic and tactile sensing devices. The integration of the data from all of these sensors operating simultaneously is the major signal for sensor fusion methodologies in robot application.

The development of an intelligent sensor for monitoring a manufacturing operation generally requires the following three hierarchical stages:

1) Determining the sensitivity of a sensor signal to the process parameters to be monitored;

2) Developing an appropriate in-process real-time signal processing method for extracting signal parameters that are rich in information about the process parameters being monitored, but relatively insensitive to other parameters; and

3) Developing a decision-making scheme that can make a decision on the process state based on the data obtained from all previous experiences as well as current sensor information.

 

Researches have developed over the years a wide variety of sensors and sensing strategies, each attempting to predict or detect a specific phenomenon during the operation of the process and in the presence of noise and other environmental contaminants. Although able to accomplish the task for a narrow set of conditions, these specific techniques have failed to be reliable to work over the range of operating conditions and environments commonly available in manufacturing facilities. Thus, researchers have begun to look at ways of collecting the maximum amount of information about the state of a process from a number of different sensors. The strategy of integrating the information from a variety of sensors is called sensor fusion. The most advantageous aspect of sensor fusion is the richness of information available to the signal processing/feature extraction and decision-making methodology employed as part of the sensor system.

Sensor fusion is a system structured to utilize many of the same elements needed for sensor fusion. These elements include the basic sensor hardware, as well as basic signal conditioning, decision-making and self-calibration and diagnostic capabilities. We may define it as a device with one or more transducer elements, signal conditioning and signal processing electronics, microcontroller and communication circuitry integrated in the same package.

 

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