Thank you very much
I'm going to introduce my paper about robust unsupervised factory activity recognition with multiple motifs.
Many companies now are looking forward to improving the productivity for assembly works.
These types of works consist of a fixed order of operation series and repeat all over the time.
Here is an example from our day.
A complete work period contains six different operations in this case.
And the order as well as the duration of those operations are predetermined by the manager.
Mostly, workers keep repeating the series of operations over and over again.
While, sometimes the worker will also do other operations, such as packing the box after it was full.
These undefined operations, we called outlier operations.
In order to improve the efficiency of workers.
Managers want to understand the actual time spent on each operation
as well as the time when the outlier operation occurs and continue.
Last year we tried to recognize the starting time of operations using unsupervised learning method
which consists of two main parts.
First, we find a characteristic data segment motif that appears only once in each period.
It's able to use three motifs to determine the duration of the middle period.
In the second step, we generate multiple particle series,
where each particle represents the starting time of an operation.
Here the first particle is generated from every changes.
As the trend change indicates possible operation start times,
which gives higher probability to find the correct operation start times.
And the next particle is tracked according to the duration of his parent operation based on the work model.
And this model can be easily built by Managers from the instruction documents.
Then, each operation series is evaluated with four scores.
Final the data is segmented according to the highest scoring features.
So in the case, we defined the operation one starts from the first particle and ends and the second particle.
While this method still has two main challenges.
the accuracy decreases when the outlier appears, which caused by the failure of tracking motifs.
And time-consuming while calculating the scores.
So let's discuss them into detail.
As for the first challenge. Since we only found a single motif last year.
It's difficult to determine the location of the outliers only by a single motif.
So, this year we look for multiple motifs.
For the best multi we discovered,
we tracked the next occurrence of the motif with the highest sensor data similarity in the following working periods,
where the similarity series is calculated from the auto-correlation of the motif candidates.
As for the other motifs, since they are not as confident as the best one,
in addition to the data similarity, the relative distance to the best motif is also compared.
Their temporal distance should be consistent in different periods.
By this way, we can easily determine if an outlier exists in an area
by comparing temporal distance in different periods.
As for the second challenge
When evaluating the particle series,
the similarity score is applied to compare sensor data similarity of same operations in different periods.
DTW it's able to compare data similarity with different length, but it's quite slow.
While in this research, we introduced action motif score.
The action motif corresponds to some basic actions that appear several times in each period.
And it can be tracked in a similar way as period motifs.
We calculate statistics features for these basic actions and
represent each operation using feature vectors.
Euclidean distance is calculated for comparing the operation similarities.
Finally, let's take a quick look at the results.
We compared our method with last year's method on six different datasets.
The average accuracy for the 'Proposed' outperforms other methods
and the computation time is also much faster.
Here is an example output comparing proposed with last year's.
Thank you for your attention.