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In the presented project, new approaches for the prevention of hand movements leading to hazards and for non-contact detection of fingers are intended to permit comprehensive and economical protection on circular saws. The basic principles may also be applied to other machines with manual loading and/or unloading. Two new detection principles are explained. The first is the distinction between skin and wood or other material by spectral analysis in the near infrared region. Using LED and photodiodes it is possible to detect fingers and hands reliable. With a kind of light curtain the intrusion into the dangerous zone near the blade can be prevented. The second principle is video image processing to detect persons, arms and fingers. In the first stage of development the detection of upper limb extremities within a defined hazard area by means of a computer based video image analysis is investigated.
The device (10) has a handrail (18) provided with an optical contactless monitoring device formed as an active sensor system, where the monitoring device is arranged in a region of a guide (14) of the handrail at a front base (16) of an escalator (12) or a moving pavement. The monitoring device has two transmission paths (28, 30) with wavelength bands that are different from each other, where one of the paths includes the handrail. Ratio or difference between signals of the paths is used for recognizing foreign bodies e.g. hands of adults and children.
The proper use of protective hoods on panel saws should reliably prevent severe injuries from (hand) contact with the blade or material kickbacks. It also should minimize long-term lung damages from fine-particle pollution. To achieve both purposes the hood must be adjusted properly by the operator for each workpiece to fit its height. After a work process is finished, the hood must be lowered down completely to the bench. Unfortunately, in practice the protective hood is fixed at a high position for most of the work time and herein loses its safety features. A system for an automatic height adjustment of the hood would increase comfort and safety. If the system can distinguish between workpieces and skin reliably, it furthermore will reduce occupational hazards for panel saw users. A functional demonstrator of such a system has been designed and implemented to show the feasibility of this approach. A specific optical sensor system is used to observe a point on the extended cut axis in front of the blade. The sensor determines the surface material reliably and measures the distance to the workpiece surface simultaneously. If the distance changes because of a workpiece fed to the machine, the control unit will set the motor-adjusted hood to the correct height. If the sensor detects skin, the hood will not be moved. In addition a camera observes the area under the hood. If there are no workpieces or offcuts left under the hood, it will be lowered back to the default position.
Entering the work envelope of an industrial robot can lead to severe injury from collisions with moving parts of the system. Conventional safety mechanisms therefore mostly restrict access to the robot using physical barriers such as walls and fences or non-contact protective devices including light curtains and laser scanners. As none of these mechanisms applies to human-robot-collaboration (HRC), a concept in which human and machine complement one another by working hand in hand, there is a rising need for safe and reliable detection of human body parts amidst background clutter. For this application camera-based systems are typically well suited. Still, safety concerns remain, owing to possible detection failures caused by environmental occlusion, extraneous light or other adverse imaging conditions. While ultrasonic proximity sensing can provide physical diversity to the system, it does not yet allow to reliably distinguish relevant objects from background objects.This work investigates a new approach to detecting relevant objects and human body parts based on acoustic holography. The approach is experimentally validated using a low-cost application-specific ultrasonic sensor system created from micro-electromechanical systems (MEMS). The presented results show that this system far outperforms conventional proximity sensors in terms of lateral imaging resolution and thus allows for more intelligent muting processes without compromising the safety of people working close to the robot. Based upon this work, a next step could be the development of a multimodal sensor systems to safeguard workers who collaborate with robots using the described ultrasonic sensor system.
Microorganisms not only contribute to the spoilage of food but can also cause illnesses through consumption. Consumer concerns and doubts about the shelf life of the products and the resulting enormous amounts of food waste have led to a demand for a rapid, robust, and non-destructive method for the detection of microorganisms, especially in the food sector. Therefore, a rapid and simple sampling method for the Raman- and infrared (IR)-microspectroscopic study of microorganisms associated with spoilage processes was developed. For subsequent evaluation pre-processing routines, as well as chemometric models for classification of spoilage microorganisms were developed. The microbiological samples are taken using a disinfectable sampling stamp and measured by microspectroscopy without the usual pre-treatments such as purification separation, washing, and centrifugation. The resulting complex multivariate data sets were pre-processed, reduced by principal component analysis, and classified by discriminant analysis. Classification of independent unlabeled test data showed that microorganisms could be classified at genus, species, and strain levels with an accuracy of 96.5 % (Raman) and 94.5 % (IR), respectively, despite large biological differences and novel sampling strategies. As bacteria are exposed to constantly changing conditions and their adaptation mechanisms may make them inaccessible to conventional measurement methods, the methods and models developed were investigated for their suitability for microorganisms exposed to stress. Compared to normal growth conditions, spectral changes in lipids, polysaccharides, nucleic acids, and proteins were observed in microorganisms exposed to stress. Models were developed to discriminate microorganisms, independent of the involvement of various stress factors and storage times. Classification of the investigated bacteria yielded accuracies of 97.6 % (Raman) and 96.6 % (IR), respectively, and a robust and meaningful model was developed to discriminate different microorganisms at the genus, species, and strain levels. The obtained results are very promising and show that the methods and models developed for the discrimination of microorganisms as well as the investigation of stress factors on microorganisms by means of Raman- and IR-microspectroscopy have the potential to be used, for example, in the food sector for the rapid determination of surface contamination.
The use of manually fed machines (e.g. table saws) bares risks of injury that are clearly above the average level of other high risk workplaces.
The wide use of such machines causes severe problems for occupational safety and implies high costs for medical treatments and accident annuities.
This thesis presents a new concept of a multispectral sensor to monitor an area in front of a danger zone to detect the user’s limbs and trigger safeguarding measures to prevent an accident in time.
The sensor concept realizes a contact-free material classification, which comprises the development of a system design and specific safety requirements with respect to international safety standards.
Furthermore, a prototypical implementation using four wavebands, which were determined for skin detection through an analysis of reflectance spectra acquired specifically for this purpose, was built.