Industry 4.0 sets a major revolution in the modern industrial landscape. This new era is characterized by the integration of connected digital technologies within manufacturing processes, commonly referred to as the Industrial Internet of Things (IIoT).
IIoT brings a new dimension of efficiency, flexibility and innovation to industry, paving the way for more efficient production through predictive maintenance, where data, industrial time series, become the driving force behind decision-making and value creation.
What is an industrial time series?
An industrial time series can be defined as a sequence of data collected at regular intervals on industrial equipment. This data is collected via sensors placed directly on industrial machines and equipment. Example: An industrial time series can be represented by the vibrations of a turbine.
They can concern a wide range of industrial fields, such as manufacturing, operations, energy management, etc.
Why generate and use industrial time series?
In the industrial sector, time series can represent a goldmine of information for companies, if properly exploited. Indeed, these data can be used to monitor, analyze and predict the behavior of industrial machines and equipment. They enable companies and organizations to improve their operations, efficiency and profitability.
The specific challenges of time series analysis
The analysis and management of time series can sometimes be complex, due to the specificities with which industrial players may be confronted. Such as:
Managing massive data
In the industrial context, temporal data can be massive, particularly when many variables are monitored at high frequency. Storing, managing and analyzing large quantities of data can be complex, requiring specific infrastructures.
Missing or incomplete data
Time series can contain missing data, which can complicate analysis and modeling. Sometimes, the lack of historical outage data can make time series analysis even more difficult.
Model scalability
Models developed for time series analysis often need to be scalable to take into account new real-time data and maintain their accuracy.
Data security
In an industrial context, data security is crucial. It is therefore essential to protect data against unauthorized access and cyber-attacks.
Thanks to its ability to predict failures in blind mode without a history of failures, and its capacity to process large volumes of data, our DiagFit software meets these challenges.
Anomaly detection in industrial time series
Anomaly detection, or fault detection, involves identifying data points, or events, that deviate significantly from expected behavior, known as the “normality space”.
These detected “novelties” may indicate problems, potential failures on production machines, or sometimes new contexts never seen before.
What are the steps involved in time series model creation?
The process of detecting anomalies in time series generally works in the following way:
1) Collecting data
The first step is to collect temporal data from sensors, measuring instruments or monitoring systems. This data can include information on production, quality, energy consumption, temperature, pressure, vibration, etc.
2) Data pre-processing
Before performing failure detection, it is often necessary to pre-process the data. This may include outlier removal, smoothing, normalization and missing data management.
3) Modeling nominal behavior
To detect failures, it is essential to first determine the “normal” behavior of the equipment through its time series. This can be done using statistical methods such as moving averages, rolling averages, or regression models to capture trends and seasonal variations.
4) Identification of anomalies
Once the model of normal behavior has been established, anomalies can be identified by comparing actual data with the model’s predictions. Data points that deviate significantly from the model are considered anomalies.
Read also: Labeling anomalies
5) Validation of anomalies
Once anomalies have been detected, they need to be validated to ensure that they are not the result of measurement errors or other temporary factors. This may involve manual examination of the data or the use of additional techniques, such as validation rules.
6) Corrective action
Once anomalies have been confirmed, corrective action can be taken. This may involve repairing faulty equipment, adjusting production processes, scheduling preventive maintenance, or taking other measures to resolve the problems identified.
6) Continuous monitoring
Failure monitoring is a continuous process. Anomalies detection models need to be regularly updated to take into account the process changes and new data.
Know more about the incremental approach.
To conclude, detecting anomalies in industrial time series helps to reduce unplanned downtime. Improve product quality, optimize maintenance and reduce costs, all of which are essential for maintaining efficient, profitable operations.
DiagFit our software dedicated to anomalies detection for industrial time series
DiagFit is the predictive maintenance software developed by Amiral Technologies to anticipate and plan machine breakdown. With DiagFit, the user has access to an interface for real-time monitoring or offline diagnosis, enabling the identification of breakdowns and their characteristics.
Thanks to a machine learning system specialized in time series management, DiagFit is able to operate without the need for breakdowns history or prior knowledge of the type of data to be processed. This is known as blind-mode failure detection.
DiagFit is also agnostic, i.e. it has been specifically designed to process any type of time-series data from any type of industrial equipment (DiagFit is not supplied with sensors but uses those present on the equipment). It is therefore the perfect tool for managing this type of series, whether cyclical or non-cyclical. DiagFit enables the creation of predictive models through a reliable, future-proof solution. These models generate substantial savings, boosting the efficiency of industrial operations.
