The industry is moving towards more advanced utilization of data from industrial equipment. This data, often in the form of complex time series, requires careful preparation and/or thorough exploration by domain experts.
When industrial users embark on analyzing their data to solve a specific problem (such as detecting specific failures, anticipating machine downtime, etc.), they may find themselves disoriented regarding the approach to take.
How to approach raw data from equipment? What elements should be examined to extract useful and actionable information? How to make the switch from analysis to value creation for the company?
In this article, we will explore some of the operations that our Data Scientists experts can carry out from your data during pilot phases on your use cases.
Analyze industrial time series
Visualization and comparison of raw data
To begin data analysis, it can be interesting to visualize the raw signals over time. This visualization allows the domain expert to become aware of signal variations, possible recurring patterns and correlations between sensor signals.
The use of simple visualization tools, such as signal comparison (being able to superimpose the signals from two or more sensors of the same nature on the same axis) or the superposition of cycles (for example in the case of a rotating machine) make it possible to visualize obvious drifts or potential correlations between signals.
Labeling of visible incidents
Thanks to the visual analysis, users who master the data, can possibly identify certain malfunctions visible in the signals. It is then possible to identify these areas of anomalies and classify them (if necessary) to generate a complete and usable dataset.
This labeling can be very useful both to :
- exclude these areas in advance from all the healthy data used for the characterization of normality, making it more relevant
- but also to provide areas of defects allowing partial validation of the normality output of the indicators learned later in the process.
Detection and outliers removal
However, depending on the nature of the signal, the removal of outliers is not trivial and requires expertise in time series.
When exploring data, it is important to spot and remove outliers that could influence the quality of certain models.
After removal, the quality of the signal is greatly improved, specifically if the target is to generate an efficient fault prediction model.
Transform data
Splitting the signal from a sensor with trend
The overall analysis of the sensor curves enables to visualize whether they present a temporal trend or not.
These trends, though natural for certain data measured, can sometimes hide abnormal variations, signs of anomalies. It may therefore be interesting to take this trend into account explicitly, in order to carry out a more complete analysis. It is then advisable to split the signal in order to obtain two curves, one with the trend and the other without. The two signals thus obtained contain important and different information, allowing a more in-depth analysis of this same signal.
As part of the generation of a predictive model from a single sensor (univariate model) it is even more interesting to analyze the curve without trend.
In the context of multivariate models, this splitting technique also makes it possible to capture inter-sensor relationships that the trend risks hiding.
The curve with trend (often associated with low frequencies) makes it possible to analyze possible correlations between the trends of two different sensors. Secondly, in the event of a breakdown, one of the possible causes could come from this de-correlation.
On the other hand, he trendless curve (often associated with high frequencies) containing the so-called more “stationary” information makes it possible to analyze variations that would not have been visible if the trend was still present.
Thanks to this splitting of the signal, we obtain a very in-depth analysis of the behavior of the same sensor.
Virtual sensors - Mathematica operators applie to one or several signals
When exploring data, it may be appropriate to perform operations on raw signals in order to extract new time series (called “Virtual Sensors”), thus revealing new exploitable information. Basic operations such as addition, subtraction, multiplication and division between two signals can be used for this purpose.
For example:
- Multiplication of the voltage and the intensity enables to obtain the electrical power (P=UxI, reminder of physics lessons in high school)
- The division of A by B can simulate a transfer function of a system or analyze the de-correlation between two sensors measuring the same data
It is also possible to go further using more complex operations such as the derivative, which can be relevant for modeling a dynamic system of the form Ẋ=AX.
For example:
- The derivative of the speed provides its acceleration
- The derivative of the volume allows you to obtain its flow rate
Conclusion
La maximisation de la valeur des données industrielles repose sur une analyse approfondie et parfois sur une transformation judicieuse des séries temporelles. Grâce à une approche rigoureuse, qui commence par la visualisation des signaux bruts et se poursuit avec le nettoyage des données ainsi que la création de capteurs virtuels, l’exploitation des données devient un levier puissant pour l’amélioration des performances des équipements industriels.
Ces opérations ne sont que le début de notre méthodologie éprouvée au fil des années sur divers cas d’usages. Dans un prochain article, nous explorerons l’analyse tridimensionnelle (capteurs, temps et fréquence) des signaux, élargissant ainsi nos perspectives pour une exploitation encore plus fine des séries temporelles industrielles.
Maximizing the value of industrial data relies on in-depth analysis and sometimes judicious transformation of time series. Thanks to a rigorous approach, which begins with the visualization of raw signals and continues with data cleaning as well as the creation of virtual sensors, the exploitation of data becomes a powerful lever for improving the performance of industrial equipment.
These operations are only the first steps of our methodology proven over the years on various use cases. In a future article, we will explore the tridimensional analysis (sensors, time and frequency) of signals, thus broadening our perspectives for even finer exploitation of industrial time series.
New release – DiagFit 3.0
With DiagFit 3.0, performing all the operations mentioned above is now at your fingertips, without the need to write a single line of code. All of the operations proposed (deletion of outliers, trend detection) are based on proprietary algorithms proven for industrial time series. The intuitive interface is designed to make life easier for users, offering smooth navigation within time series. Thanks to guidance supported by our solid methodology, users explore their data to extract relevent information, essential to the implementation of an effective predictive maintenance approach.