The development of some instrumentation and measurement systems poses significant challenges due to their continuous interaction with environments that are both harsh and highly dynamic. They are often described as “Untestable” because their testing is sometimes expensive, time-consuming, and infeasible. One example is oil-spill measurement systems that aim to measure the thickness of oil floating on the water surface in open water environments. In contrast to analog sensors relying on calibration functions, such integrated measurement systems use algorithms with multiple inputs to produce their measurement. Intending to facilitate the development of such systems, we shed light on virtual testing methods designed for testing Cyber-physical Systems (CPSs). CPSs are smart and autonomous systems composed of collaborating computational elements (software) that control physical entities (hardware). Effective validation and verification techniques are required to confirm their correctness. These methods were applied to test continuous controllers in the automotive domain. In this article, we review some of these testing methods and provide a framework for applying them to measurement systems that are difficult to test in real life. We provide a case study based on an oil spill measurement system that relies on multiple sensors to estimate the oil thickness in open water environments. Applying this approach creates a reduced set of test cases to be applied in real field testing reducing its cost and time.

This work evaluates the performance of a non-supervised clustering method for identifying abrupt changepoints in streaming sensor data. The proposed method utilizes the Jenks Natural Breaks (JNB) algorithm, applied in near real-time using sliding temporal windows to analyze sections of sensor data and identify instances of significant phase changes. It is suitable for sensing applications that rely on detecting instantaneous changes in the sensed data for fast decisions, such as fire alarms, fault detection, and activity recognition. The method was applied to a custom dataset from twelve electrodes transitioning between different materials. Performance was evaluated based on detection accuracy and delay comparisons. Results demonstrate that applying JNB in a sliding window with a step size of half its length achieves the highest detection accuracy and the lowest error delay compared to non-overlapping windows.