Optimizing Uptime and Lifespan of Machines with Predictive Maintenance


Introducing AI to Machine Maintenance: A Game-Changer

For a gearbox manufacturer, we analysed gearbox sensor data and applied AI algorithms with the purpose of preventively detecting anomalies in the data, allowing for timely maintenance interventions and thus improving overall machinery performance.

Gearbox manufacturers and by extension also machine manufacturers face critical challenges: high maintenance costs, significant losses from downtime, and managing large volumes of sensor data. This project tackled how to structure vast amounts of data for actionable insights and what to do when anomalous data is rare or even absent.

The solution revolves around the development of an AI model capable of distinguishing between normal and anomalous data, where the integration of synthetic anomalies plays a crucial role in model testing. The impact of implementing this AI-driven anomaly detection model is substantial. It leads to maximised machine uptime, extended machinery lifespans, and significant cost savings by minimising downtime and preventing escalations of minor issues into major faults. This project highlights the potential of AI in transforming machine maintenance and operational efficiency, setting a new standard for proactive maintenance strategies.

Today’s Critical Challenges of Machine Developers

For machine manufacturers, maintenance and operational efficiency are critical. In this case, two main issues loom large: the high costs of maintaining the machinery and the economic effects of unexpected downtimes. Such downtimes lead to direct repair expenses and significant revenue losses during inactive periods. Additionally, the complexity of the data generated by these machines adds another layer of challenge.

Our client is at a pivotal moment, capturing vast amounts of sensor data with the potential for extracting insights for optimising operational processes. However, utilising this data effectively is not straightforward. It prompts questions about the data’s quality and suitability for advanced AI applications: Can it reveal patterns that warrant AI investment? More importantly, can we convert this data into actionable intelligence that not only anticipates but also prevents downtime, thus lowering maintenance expenses and boosting machine efficiency?

Therefore, the issue extends beyond the technical to also include strategic considerations. It demands a detailed understanding of what the data offers and its constraints.

A Comprehensive AI Solution for Anomaly Detection

Data Deep Dive: Exploring the Complex Data

We started off with an Exploratory Data Analysis (EDA) to sift through the extensive datasets. This phase was undertaken without a specific direction or predetermined use case, allowing for an open-ended exploration of the data. The EDA had two primary goals: firstly, to evaluate the data's suitability for machine learning applications, ensuring it was robust and comprehensive enough for such advanced analysis. Secondly, it aimed to uncover potential use cases that warranted further investigation, laying the groundwork for targeted, in-depth analyses. From the analysis, anomaly detection proved to be the most promising use case.

Building the Core: AI Models for Anomaly Detection

Our anomaly detection approach is centred around an AI model, which is trained on data from machinery operating under normal conditions. This model creates a baseline, being able to forecast the expected behaviour of the machinery in standard operational scenarios. Its strength lies in distinguishing between typical operational fluctuations and actual anomalies. If the real-time data significantly differs from the model’s forecasts—crossing a pre-determined threshold for a certain duration—we consider this a potential anomaly. This method facilitates the early identification of problems, enabling prompt maintenance actions.

To validate the effectiveness of our model, however, it is essential to test it on a dataset that includes anomalies. One common hurdle in anomaly detection is the lack of (sufficient) real anomaly-containing data. Sometimes, the necessary data for certain anomalies doesn't exist because those specific anomalies have not occurred yet, for example in case of a new type of machinery. To address this, we introduced synthetic anomalies into our data. This process entails modifying normal data to include simulated failures, creating a comprehensive test dataset to assess the model’s ability to detect anomalies. This step is crucial for refining our anomaly detection capabilities, ensuring the model can spot deviations accurately, even in the absence of actual anomaly data. But, introducing synthetic anomalies into complex time-series data sets presents its own set of challenges.

Decoding Complex Data: The Role of Fourier Transformations

A key element in our approach was a Fourier transformation, which allows us to simplify complex time-series data by decomposing time series into their basic frequency components. Why is this valuable? To grasp the utility of the Fourier transformation in our project, let's consider a straightforward analogy: a graphic equalizer in a music system.

Imagine listening to a song, which is essentially a blend of various sounds - bass, vocals, and treble - coming together over time. When you look at this as a waveform, you see a complex pattern of loudness changing over time. It's challenging to pick apart the bass from the vocals or the treble just by looking at these waves. Enter the graphic equalizer, a tool that employs Fourier transformations. This technique takes the song's waveform (our time series of sound waves) and breaks it down into its basic components, categorised by their frequencies: low frequencies for the bass, mid frequencies for vocals, and high frequencies for treble. By analysing the song in this frequency domain, it becomes straightforward to isolate and adjust specific elements of the music.

In our project, the Fourier transformation serves a similar purpose. By transforming the complex time-series data from the sensors into the frequency domain, we can clearly identify and act on different components of the data—much like adjusting frequencies for the perfect sound balance in a song. By adjusting the amplitude of certain frequencies, we were able to simulate anomalies within the data. The altered data, now containing these synthetic anomalies, is then combined back into the time series format using the inverse Fourier transformation. This dataset containing synthetic anomalies then can be used as a test set to ensure our model can accurately identify anomalies. This transition is crucial because it reveals hidden anomalies in the frequency domain that might not be apparent in the time-sequence data.

For those interested in exploring the technical details and theory behind this approach, one of our experienced engineers has thoroughly discussed these concepts in a blog post about.

Refining Our Approach: Advances in Our Model

We further improved the anomaly detection model by adding more variables and testing different forecasting periods. This phase also saw the setup of a cloud infrastructure for data processing, marking a step forward in refining our predictive tools.

This holistic and accessible approach establishes a solid framework for using extensive datasets to improve machinery maintenance and efficiency. It reflects our dedication to leveraging advanced AI solutions to tackle the complex challenges faced by the industry, ensuring reliability and operational excellence.

Results and Impact

The deployment of our anomaly detection model has significantly enhanced machinery performance, increasing uptime and extending its operational life. By using Fourier transformations to simulate anomalies, we've been able to thoroughly validate our model against numerous potential failure scenarios, guaranteeing its effectiveness in identifying actual anomalies. This proactive strategy is vital in reducing downtime and ensuring machinery longevity through timely and precise maintenance interventions. Moreover, the model's early warning system promotes preemptive maintenance, thereby preventing minor issues from developing into major complications.

This project underscores the potential of AI in smart manufacturing and maintenance practices. We invite organisations keen on exploring AI solutions to reach out and discuss how these technologies can be tailored to meet their specific operational challenges, driving efficiency, and innovation in their processes.

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