Order Reconstruction Data Analysis Challenge

OVERVIEW

Machinery such as turbines and bearings undergo subtle changes in their vibration signals and signatures, long before catastrophic failure occurs. Being able to track this degradation process accurately is crucial for predictive maintenance and avoiding downtime.

In this Coding Challenge, TII is providing high-frequency vibration recordings and tachometer signals from a controlled degradation test. However, the file names have been randomly permuted: scrambled up, to hide the true order of the recordings. Your task is to reconstruct the true chronological order of the data files. By developing methods to extract the features that evolve consistently over time, Solvers are asked to reassemble the degradation timeline of the test setup.

This task is open to individuals, teams, and organizations with data expertise and the curiosity to piece back together the true order of the files. Successful approaches may include: signal processing, statistical degradation, or AI/ML-based sequence reconstruction.

Take note: Solvers are warned that the dataset for this Challenge, available for download in the Datasets & Scoring section, is a zipped file (.zip format) – file size is around 55 megabytes. This zipped file then has a larger storage footprint when unzipped. Please be aware of the file size implications, both on initial download speed, bandwidth/download allowance, and storage on your computer.

Submit your order reconstruction to compete against other Solvers for the top Prize! The total award pool for this Challenge is $26,000 USD, with awards of $12,000 USD for 1^st Place and $9,000 shared by up to 5 runners-up. For the top performer across both TII Coding Challenges (including this and the Gearbox Remaining Useful Life (RUL) Prediction Challenge), there is an additional $5,000 USD bonus prize available.

Winning Solvers or teams will also be invited to an exclusive TII event slated for early 2026, with details to follow.

 

Your IP Rights are protected in this Coding Challenge; TII must pay you an award to obtain them.

The best solutions have the opportunity to win $12,000 for 1^st place and $9,000 shared by up to 5 runners-up for their performance on the leaderboard together with their submission quality. There is a bonus award of $5,000 on offer for the top performer across both TII Coding Challenges. The Challenge requires a written proposal to be submitted, and requisite files meeting the requirements to be uploaded as attachments.

Awards will be contingent upon the theoretical evaluation of the proposal by TII against the Solution Requirements and your ranking on the private leaderboard. To receive an Award, Solvers are required to grant non-exclusive rights to the Intellectual Property (IP) in their proposed solution. There is no assignment of IP Rights with this challenge. Solvers will retain all rights to any proposal not Awarded.

 

Submissions to this Challenge must be received by 11:59 PM (US Eastern Time) on November 28, 2025.

Please review the later Participation Guidance section before submitting a proposal.

- Login or register your interest to start solving! -

 

ABOUT THE SEEKER & ELIGIBILITY

Technology Innovation Institute (TII) is a global scientific research center attracting the world’s foremost scientists and researchers. TII leads worldwide advances in artificial intelligence, autonomous robotics, quantum computing, cryptography and quantum communications, directed energy, secure communication, smart devices, advanced materials, and propulsion and space technologies, and biotechnology fields.

TII belongs to the Abu Dhabi Government’s Advanced Technology Research Council (ATRC), which oversees the technology research.

This Challenge opportunity invites innovators, start-ups, research institutes, and university students from anywhere in the world with the skills, resources and knowledge, who are all eligible to participate in the Challenge, except:

• Employees of the TII and its affiliates; its parent company or other subsidiaries of the parent company;
• Employees of agents or suppliers of the TII or any of its affiliates, who are professionally connected with the Challenge or its administration;
• Members of the immediate families or households of the aforementioned;
• Any person or entity registered or ordinarily resident in a country that is on a sanctions list at any time during this Challenge (including, but not limited to, the Sanctions Lists maintained by the United States, the United Nations and the European Union).

 

THE CHALLENGE

Background

Bearings are among the most critical components in rotating machinery, enabling smooth motion and distributing loads. Over time, bearings degrade, and this gradual process is reflected in the vibration signals of the machine. The ability to detect, track, and interpret these evolving vibration patterns is essential for predictive maintenance, improving safety, and avoiding costly downtime.

Traditionally, condition monitoring systems provide sequential, labelled, and timestamped datasets. This allows engineers to observe the degradation process in order. However, in this Challenge, participants are given a scrambled dataset – the file names have been randomly permuted, hiding the true chronological order of the recordings.

The task is to reconstruct the true timeline of bearing degradation by analyzing high-frequency vibration and tachometer signals from a helicopter’s turboshaft engine. In real-world conditions, changing loads and operational variability make vibration data stochastic. Despite this, degradation is unidirectional – bearings do not ‘heal themselves’. Thus, participants must identify consistent features that evolve over time and use them to reassemble the correct order of events.

Why It Matters

In aviation and other industries, failures in rotating machinery (such as turbines and bearings) can have catastrophic consequences. Subtle changes in vibration signals often occur long before visible failure. Had the monitoring system not raised an alert in this real-world dataset, continued operation could have resulted in a mishap. Developing methods to detect, track, and predict degradation enables safer operations and better scheduling of maintenance, while reducing downtime and cost.

The Setup

Your Challenge is to develop a method to reorder the provided dataset files, estimating the true chronological sequence of the bearing degradation run.

• You are provided with a set of vibration and tachometer recordings from a helicopter turboshaft engine.
• The file order has been randomized to obscure the true chronological progression.
• Your task is to reconstruct the original sequence of recordings, for files {1, …, N}, estimating the true degradation timeline.

Specifically, in this Coding Challenge, Solvers must submit a predicted permutation vector – mapping each input file (from the dataset provided) to its estimated position in the reconstructed timeline.

Datasets and Scoring

Please click the following link to begin a download of the dataset: DOWNLOAD HERE

This dataset also includes example submissions for you to see the desired format.

Solvers should note that this file, even zipped, is approximately 55mb, and when unzipped is much larger.

The dataset includes N time-history files of vibration data, each containing:

• Acceleration time series sampled at 93,750 Hz (single channel)
• Zero-cross tachometer timestamps (zct)
  
• A fixed gear ratio of 5.095238095 from tachometer shaft to turbine shaft → nominal turbine speed ≈ 536.27 Hz
• Bearing geometry factors (cage, ball, inner race, outer race): [0.43, 7.05, 10.78, 8.22], yielding nominal fault-band centers at [231, 3781, 5781, 4408] Hz (based on mean turbine speed).

Files provided are file_1.csv, file_2.csv, …, file_N.csv (in a random, scrambled order).

Channels per file:

• Acceleration (1 channel, 93 750 Hz)
• Tachometer zero-cross timestamps (zct)

Fixed parameters table:

 

All files originate from an operating aircraft, but are presented in a random order. Your task is to recover the true chronological sequence by ranking the files correctly.

Each file represents a short time-history of high-frequency vibration and tachometer data. Degradation features in bearings are expected to evolve monotonically, either increasing (e.g. RMS energy, fault-band amplitudes) or changing systematically in frequency content.

This Coding Challenge’s primary evaluation metric is the Spearman footrule distance, a measure of how far your predicted permutation or file order is from the true, chronological order. A perfect reconstruction would score 0 on the leaderboard. 50% of the predictions will contribute to the public leaderboard and 50% to the private leaderboard.

Your attempts to solve the true order will be presented on a public leaderboard, where you can compare and compete with other Solvers!

Solvers are welcome to explore a variety of approaches, as long as they include robust methodologies and executable code. For instance, you could try:

• Signal processing
• Statistical degradation modelling
• Machine learning models or AI tools and techniques

Ultimately, any solution must balance robustness, interpretability, and computational efficiency – in order to be best placed to inform real-world diagnostic systems in key industries.

 

GETTING STARTED

1. Download the files. Please note the size of the zipped file (approximately 55mb), which becomes much larger when unzipped. Be sure to double check your connection, bandwidth allowance, and computer storage before downloading
2. Extract features that should evolve monotonically.
3. Rank files by your chosen feature(s) to estimate the true chronological order
4. Create and upload your submission using the attachments section of the form, using the correct file type (.csv), naming convention, and including methodology description and executable code.
5. Check the leaderboard after submitting.
6. Use your score, status, or competition to guide your next submission!

 

SOLUTION REQUIREMENTS

Submissions must provide the predicted sequence ordering, executable code, and a short, technical description of the method used.

TII is primarily interested in solutions that meet the following must-have requirements:

• Private Leaderboard ranking: as the quantitative measure of the Challenge, your leaderboard score will be taken into account when evaluating your submission. The closer the score is to 0, the better/more highly you will rank – using the Spearman Footrule Distance. A score of 0 indicates a perfect chronological order of the dataset. You can view the score for every Coding Submission on the 'My Submissions' tab of the Leaderboard.
  
  Pairwise Order Accuracy will be used as a secondary, tie-breaker metric:

 

 

• Correct format of submission
  • Provide a .csv file, titled submission.csv, containing a single column with header “prediction” containing an array of N integers – representing the predicted chronological order of the files for the bearing degradation (so that e.g. the first integer in the column represents the predicted position in the true chronological order of file_1.csv).

The permutation/order you submit must include all N files, with no duplicates or omissions, in the format of 1-N (e.g., {1, …, N} – there is no need to include 0 or summary rows.

• • Provide executable code, either through a link, attachment, or in the form text (please denote).
  • Submit a concise, written explanation of your methodology, with room to add attachments.
• Soundness of your methodology:
  • The technical validity and merit demonstrated by your methodology will be considered in making the final decision.

Please note again: TII will consider both quantitative and qualitative measures to determine success in this Challenge.

 

Things to Avoid

Solvers are not allowed to game the Challenge, manually relabel, or in any other way ‘hack’ the Challenge. TII welcomes any and all innovative approaches that rely on proven code, and robust methodologies, but will not consider anyone acting outside the spirit of the Challenge.
 

This Coding Challenge has the following features:

1. Your IP Rights are protected; TII must pay you an award to obtain them.
2. The best solutions have the opportunity to win $12,000 for 1^st place and $9,000 shared by up to 5 runners-up for their performance on the leaderboard together with their submission quality. There is a bonus award of $5,000 on offer for the top performer across both TII Coding Challenges.
3. The Challenge requires a written proposal to be submitted, and requisite files meeting the requirements to be uploaded as attachments.
4. Awards will be contingent upon the theoretical evaluation of the proposal by TII against the Solution Requirements and your ranking on the private leaderboard.
5. To receive an Award, Solvers are required to grant non-exclusive rights to the Intellectual Property (IP) in their proposed solution. Solvers will retain all rights to any proposal not Awarded.

 

YOUR SUBMISSION

To enter the Challenge and be considered for the leaderboard and award, you must submit the following:

Prediction file
Type: .csv file, uploaded as attachment to the submission form
Naming convention: submission.csv
Data range: Submit files from {1, …, N}, no need to include 0 or summary rows.
Format:

prediction
p_1
p_2
...
p_N

[Where p_i represents the predicted position in the true chronological order of file_i.csv. Predicted positions should be in {1, …, N}. The permutation/order you submit must include all N files, with no duplicates or omissions. Files that are not structured in this manner or with this file type will not be picked up by the leaderboard and will not be considered.]

Executable Code:
Type: GitHub repository, library, distribution package, Jupyter notebook uploaded as links in relevant form field, or uploaded as attachment to the submission form.

Methodology description:
Type: Uploaded in PDF or text file format as an attachment to the submission form.
Details: Written answer describing how you developed your solution, your modelling approach, any difference from previous approaches, and any assumptions or observations.

You will also be asked about your Participation Type (Individual or Organization), and your relevant Experience in the submission form. Please ensure you answer the Experience point in at least one of your submissions.

Remember, you can submit up to 5 submissions per day (UTC), with these being reflected on the public leaderboard so you can track your performance.

 

PARTICIPATION GUIDANCE

1. Submission Close Date: Submissions to this Challenge must be received by 11:59 PM (US Eastern Time) on November 28, 2025.
2. Late submissions: Late submissions will not be considered.
3. Multiple submissions allowed: In this Challenge, multiple submissions by the same Solver/team/organization are encouraged: as you optimize your model, the findings/data should improve and you can resubmit to the Challenge. You can check your performance against the regularly-updated leaderboard on the Challenge.
4. Evaluation notification steps: After the Challenge submission close date, TII will review and select the winning proposals according to the timeline in the Challenge header. Everyone who submits a proposal will be notified about the status of their submissions.
5. Use of AI: Please note that any submissions produced solely with generative AI are not of interest.
6. Learn more: Find out more about participation in Innocentive Challenges.

 

Interested in Coding Challenges? We’re also running another opportunity with TII in parallel – the Gearbox Remaining Useful Life (RUL) Prediction Challenge.

Can you predict exactly when a gearbox will fail? This is a rare opportunity to work with full-lifecycle degradation data from a high-fidelity gearbox test rig (complete with vibration, torque, speed, and acoustic signals) and determine the set-up’s remaining useful life (RUL).

Using rich multi-sensor data from five prior degradation runs for training, you’ll build a model to predict how much longer the gearbox will last in a sixth run at five key points in time before failure.

Think you can see the failure coming before it happens? Get started with TII’s Gearbox RUL Prediction Challenge!